def getPeaks(d, alg, hdr, fmt, mask, times, env, run, j):
"""Finds peaks within an event, and returns the event information, peaks found, and hits found
Arguments:
d -- psana.Detector() of this experiment's detector
alg -- the algorithm used to find peaks
hdr -- Title row for printed chart of peaks found
fmt -- Locations of peaks found for printed chart
mask -- the detector mask
times -- all the events for this run
env -- ds.env()
run -- ds.runs().next(), the run information
j -- this event's number
"""
evt = run.event(times[j])
try:
nda = d.calib(evt) * mask
except TypeError:
nda = d.calib(evt)
if (nda is not None):
peaks = alg.peak_finder_v3r3(nda, rank=3, r0=3, dr=2, nsigm=5)
numPeaksFound = len(peaks)
alg = PA()
thr = 20
numpix = alg.number_of_pix_above_thr(nda, thr)
#totint = alg.intensity_of_pix_above_thr(nda, thr)
return [evt, nda, peaks, numPeaksFound, numpix]
else:
return [None, None, None, None, None]
def __init__(self, parent, gateName, tofs, tofl, tofs_dict, tofl_dict):
self.parent = parent
self.gateName = gateName
self.tofs = tofs
self.tofl = tofl
self.tofs_dict = tofs_dict
self.tofl_dict = tofl_dict
self.tofbin = self.parent.monitor_params['t']['bin']
print self.tofs_dict, self.tofl_dict
self.tof_ind = (self.parent.TaxisM > self.tofs) & (self.parent.TaxisM <
self.tofl)
self.particle = self.gateName
self.extractE = extractE
self.alg = PyAlgos()
self.alg.set_peak_selection_pars(npix_min=self.parent.npix_min,
npix_max=self.parent.npix_max,
amax_thr=self.parent.amax_thr,
atot_thr=self.parent.atot_thr,
son_min=self.parent.son_min)
self.init_vars()
def __init__(self, source, monitor_params):
super(Coin, self).__init__(map_func=self.process_data,
reduce_func=self.reduce,save_func=self.save_data,
source=source, monitor_params=monitor_params)
import psana
self.monitor_params = monitor_params
self.init_params(monitor_params)
self.e_cols = self.eimg_center_y - self.e_radius
self.e_coll = self.eimg_center_y + self.e_radius+1
self.e_rows = self.eimg_center_x - self.e_radius
self.e_rowl = self.eimg_center_x + self.e_radius+1
self.e_center_y = self.eimg_center_y
self.e_center_x = self.eimg_center_x
self.mask = np.ones([1024,1024])
self.mask[350:650, 350:650] = 0
self.mask[:, :350] = 0
#self.alg = PyAlgos(mask = self.mask)
self.alg = PyAlgos()
self.alg.set_peak_selection_pars(npix_min=self.npix_min, npix_max=self.npix_max, amax_thr=self.amax_thr, atot_thr=self.atot_thr, son_min=self.son_min)
self.speed_rep_int = self.params_gen['speed_report_interval']
self.old_time = time.time()
self.time = None
print('Starting worker: {0}.'.format(self.mpi_rank))
sys.stdout.flush()
return
def __init__(self, parent, gateName, tof1s, tof1l, tof2s, tof2l, tof3s,
tof3l, thresh1_n3n1, thresh2_n3n1, thresh1_n3n2, thresh2_n3n2,
ang_f):
self.parent = parent
self.gateName = gateName
self.tof1s = tof1s
self.tof1l = tof1l
self.tof2s = tof2s
self.tof2l = tof2l
self.tof3s = tof3s
self.tof3l = tof3l
self.extractE = extractE
self.PiPiCo_ind1 = (self.parent.TaxisM >
self.tof1s) & (self.parent.TaxisM < self.tof1l)
self.PiPiCo_ind2 = (self.parent.TaxisM >
self.tof2s) & (self.parent.TaxisM < self.tof2l)
self.PiPiCo_ind3 = (self.parent.TaxisM >
self.tof3s) & (self.parent.TaxisM < self.tof3l)
self.thresh1 = {}
self.thresh2 = {}
self.thresh1['n3n1'] = thresh1_n3n1
self.thresh1['n3n2'] = thresh1_n3n2
self.thresh2['n3n1'] = thresh2_n3n1
self.thresh2['n3n2'] = thresh2_n3n2
self.ang_f = ang_f
self.cos_theta_f1 = np.cos(0.5 * self.ang_f * np.pi / 180)
self.cos_theta_f2 = np.cos((90 - 0.5 * self.ang_f) * np.pi / 180)
self.cos_theta_f3 = -np.cos((90 - 0.5 * self.ang_f) * np.pi / 180)
self.cos_theta_f4 = -np.cos(0.5 * self.ang_f * np.pi / 180)
self.particle1 = self.gateName[:2]
self.particle2 = self.gateName[2:4]
self.particle3 = self.gateName[4:6]
self.alg = PyAlgos()
self.alg.set_peak_selection_pars(npix_min=self.parent.npix_min,
npix_max=self.parent.npix_max,
amax_thr=self.parent.amax_thr,
atot_thr=self.parent.atot_thr,
son_min=self.parent.son_min)
self.init_vars()
class PeakFinding(object):
def __init__(self, parent = None):
self.parent = parent
self.d9 = Dock("Peak Finder", size=(1, 1))
## Dock 9: Peak finder
self.w10 = ParameterTree()
self.d9.addWidget(self.w10)
#self.w11 = pg.LayoutWidget()
#self.generatePowderBtn = QtGui.QPushButton('Generate Powder')
#self.launchBtn = QtGui.QPushButton('Launch peak finder')
#self.w11.addWidget(self.launchBtn, row=0,col=0)
#self.w11.addWidget(self.generatePowderBtn, row=0, col=0)
#self.d9.addWidget(self.w11)
self.userUpdate = None
self.doingUpdate = False
# Peak finding
self.hitParam_grp = 'Peak finder'
self.hitParam_showPeaks_str = 'Show peaks found'
self.hitParam_algorithm_str = 'Algorithm'
# algorithm 0
self.hitParam_algorithm0_str = 'None'
# algorithm 1
self.hitParam_alg1_npix_min_str = 'npix_min'
self.hitParam_alg1_npix_max_str = 'npix_max'
self.hitParam_alg1_amax_thr_str = 'amax_thr'
self.hitParam_alg1_atot_thr_str = 'atot_thr'
self.hitParam_alg1_son_min_str = 'son_min'
self.hitParam_algorithm1_str = 'Droplet'
self.hitParam_alg1_thr_low_str = 'thr_low'
self.hitParam_alg1_thr_high_str = 'thr_high'
self.hitParam_alg1_rank_str = 'rank'
self.hitParam_alg1_radius_str = 'radius'
self.hitParam_alg1_dr_str = 'dr'
# algorithm 2
self.hitParam_alg2_npix_min_str = 'npix_min'
self.hitParam_alg2_npix_max_str = 'npix_max'
self.hitParam_alg2_amax_thr_str = 'amax_thr'
self.hitParam_alg2_atot_thr_str = 'atot_thr'
self.hitParam_alg2_son_min_str = 'son_min'
self.hitParam_algorithm2_str = 'FloodFill'
self.hitParam_alg2_thr_str = 'thr'
self.hitParam_alg2_r0_str = 'r0'
self.hitParam_alg2_dr_str = 'dr'
# algorithm 3
self.hitParam_alg3_npix_min_str = 'npix_min'
self.hitParam_alg3_npix_max_str = 'npix_max'
self.hitParam_alg3_amax_thr_str = 'amax_thr'
self.hitParam_alg3_atot_thr_str = 'atot_thr'
self.hitParam_alg3_son_min_str = 'son_min'
self.hitParam_algorithm3_str = 'Ranker'
self.hitParam_alg3_rank_str = 'rank'
self.hitParam_alg3_r0_str = 'r0'
self.hitParam_alg3_dr_str = 'dr'
# algorithm 4
self.hitParam_alg4_npix_min_str = 'npix_min'
self.hitParam_alg4_npix_max_str = 'npix_max'
self.hitParam_alg4_amax_thr_str = 'amax_thr'
self.hitParam_alg4_atot_thr_str = 'atot_thr'
self.hitParam_alg4_son_min_str = 'son_min'
self.hitParam_algorithm4_str = 'iDroplet'
self.hitParam_alg4_thr_low_str = 'thr_low'
self.hitParam_alg4_thr_high_str = 'thr_high'
self.hitParam_alg4_rank_str = 'rank'
self.hitParam_alg4_r0_str = 'radius'
self.hitParam_alg4_dr_str = 'dr'
self.hitParam_outDir_str = 'Output directory'
self.hitParam_runs_str = 'Run(s)'
self.hitParam_queue_str = 'queue'
self.hitParam_cpu_str = 'CPUs'
self.hitParam_psanaq_str = 'psanaq'
self.hitParam_psnehq_str = 'psnehq'
self.hitParam_psfehq_str = 'psfehq'
self.hitParam_psnehprioq_str = 'psnehprioq'
self.hitParam_psfehprioq_str = 'psfehprioq'
self.hitParam_psnehhiprioq_str = 'psnehhiprioq'
self.hitParam_psfehhiprioq_str = 'psfehhiprioq'
self.hitParam_psdebugq_str = 'psdebugq'
self.hitParam_noe_str = 'Number of events to process'
self.hitParam_threshold_str = 'Indexable number of peaks'
self.hitParam_launch_str = 'Launch peak finder'
self.hitParam_extra_str = 'Extra parameters'
self.save_minPeaks_str = 'Minimum number of peaks'
self.save_maxPeaks_str = 'Maximum number of peaks'
self.save_minRes_str = 'Minimum resolution (pixels)'
self.save_sample_str = 'Sample name'
self.showPeaks = True
self.peaks = None
self.numPeaksFound = 0
self.algorithm = 0
self.algInitDone = False
self.peaksMaxRes = 0
self.classify = False
self.hitParam_alg1_npix_min = 2.
self.hitParam_alg1_npix_max = 20.
self.hitParam_alg1_amax_thr = 0.
self.hitParam_alg1_atot_thr = 1000.
self.hitParam_alg1_son_min = 7.
self.hitParam_alg1_thr_low = 250.
self.hitParam_alg1_thr_high = 600.
self.hitParam_alg1_rank = 2
self.hitParam_alg1_radius = 2
self.hitParam_alg1_dr = 1
# self.hitParam_alg2_npix_min = 1.
# self.hitParam_alg2_npix_max = 5000.
# self.hitParam_alg2_amax_thr = 1.
# self.hitParam_alg2_atot_thr = 1.
# self.hitParam_alg2_son_min = 1.
# self.hitParam_alg2_thr = 10.
# self.hitParam_alg2_r0 = 1.
# self.hitParam_alg2_dr = 0.05
# self.hitParam_alg3_npix_min = 5.
# self.hitParam_alg3_npix_max = 5000.
# self.hitParam_alg3_amax_thr = 0.
# self.hitParam_alg3_atot_thr = 0.
# self.hitParam_alg3_son_min = 4.
# self.hitParam_alg3_rank = 3
# self.hitParam_alg3_r0 = 5.
# self.hitParam_alg3_dr = 0.05
# self.hitParam_alg4_npix_min = 1.
# self.hitParam_alg4_npix_max = 45.
# self.hitParam_alg4_amax_thr = 800.
# self.hitParam_alg4_atot_thr = 0
# self.hitParam_alg4_son_min = 7.
# self.hitParam_alg4_thr_low = 200.
# self.hitParam_alg4_thr_high = self.hitParam_alg1_thr_high
# self.hitParam_alg4_rank = 3
# self.hitParam_alg4_r0 = 2
# self.hitParam_alg4_dr = 1
self.hitParam_outDir = self.parent.psocakeDir
self.hitParam_outDir_overridden = False
self.hitParam_runs = ''
self.hitParam_queue = self.hitParam_psanaq_str
self.hitParam_cpus = 24
self.hitParam_noe = -1
self.hitParam_threshold = 15 # usually crystals with less than 15 peaks are not indexable
self.minPeaks = 15
self.maxPeaks = 2048
self.minRes = -1
self.sample = 'sample'
self.profile = 0
self.hitParam_extra = ''
self.params = [
{'name': self.hitParam_grp, 'type': 'group', 'children': [
{'name': self.hitParam_showPeaks_str, 'type': 'bool', 'value': self.showPeaks,
'tip': "Show peaks found shot-to-shot"},
{'name': self.hitParam_algorithm_str, 'type': 'list', 'values': {self.hitParam_algorithm1_str: 1,
self.hitParam_algorithm0_str: 0},
'value': self.algorithm},
{'name': self.hitParam_algorithm1_str, 'visible': True, 'expanded': False, 'type': 'str', 'value': "",
'readonly': True, 'children': [
{'name': self.hitParam_alg1_npix_min_str, 'type': 'float', 'value': self.hitParam_alg1_npix_min,
'tip': "Only keep the peak if number of pixels above thr_low is above this value"},
{'name': self.hitParam_alg1_npix_max_str, 'type': 'float', 'value': self.hitParam_alg1_npix_max,
'tip': "Only keep the peak if number of pixels above thr_low is below this value"},
{'name': self.hitParam_alg1_amax_thr_str, 'type': 'float', 'value': self.hitParam_alg1_amax_thr,
'tip': "Only keep the peak if max value is above this value"},
{'name': self.hitParam_alg1_atot_thr_str, 'type': 'float', 'value': self.hitParam_alg1_atot_thr,
'tip': "Only keep the peak if integral inside region of interest is above this value"},
{'name': self.hitParam_alg1_son_min_str, 'type': 'float', 'value': self.hitParam_alg1_son_min,
'tip': "Only keep the peak if signal-over-noise is above this value"},
{'name': self.hitParam_alg1_thr_low_str, 'type': 'float', 'value': self.hitParam_alg1_thr_low,
'tip': "Grow a seed peak if above this value"},
{'name': self.hitParam_alg1_thr_high_str, 'type': 'float', 'value': self.hitParam_alg1_thr_high,
'tip': "Start a seed peak if above this value"},
{'name': self.hitParam_alg1_rank_str, 'type': 'int', 'value': self.hitParam_alg1_rank,
'tip': "region of integration is a square, (2r+1)x(2r+1)"},
{'name': self.hitParam_alg1_radius_str, 'type': 'int', 'value': self.hitParam_alg1_radius,
'tip': "region inside the region of interest"},
{'name': self.hitParam_alg1_dr_str, 'type': 'float', 'value': self.hitParam_alg1_dr,
'tip': "background region outside the region of interest"},
]},
{'name': self.save_minPeaks_str, 'type': 'int', 'value': self.minPeaks,
'tip': "Index only if there are more Bragg peaks found"},
{'name': self.save_maxPeaks_str, 'type': 'int', 'value': self.maxPeaks,
'tip': "Index only if there are less Bragg peaks found"},
{'name': self.save_minRes_str, 'type': 'int', 'value': self.minRes,
'tip': "Index only if Bragg peak resolution is at least this"},
{'name': self.save_sample_str, 'type': 'str', 'value': self.sample,
'tip': "Sample name saved inside cxi"},
{'name': self.hitParam_outDir_str, 'type': 'str', 'value': self.hitParam_outDir},
{'name': self.hitParam_runs_str, 'type': 'str', 'value': self.hitParam_runs},
{'name': self.hitParam_queue_str, 'type': 'list', 'values': {self.hitParam_psfehhiprioq_str: 'psfehhiprioq',
self.hitParam_psnehhiprioq_str: 'psnehhiprioq',
self.hitParam_psfehprioq_str: 'psfehprioq',
self.hitParam_psnehprioq_str: 'psnehprioq',
self.hitParam_psfehq_str: 'psfehq',
self.hitParam_psnehq_str: 'psnehq',
self.hitParam_psanaq_str: 'psanaq',
self.hitParam_psdebugq_str: 'psdebugq'},
'value': self.hitParam_queue, 'tip': "Choose queue"},
{'name': self.hitParam_cpu_str, 'type': 'int', 'value': self.hitParam_cpus},
{'name': self.hitParam_noe_str, 'type': 'int', 'value': self.hitParam_noe,
'tip': "number of events to process, default=-1 means process all events"},
{'name': self.hitParam_extra_str, 'type': 'str', 'value': self.hitParam_extra, 'tip': "Extra peak finding flags"},
{'name': self.hitParam_launch_str, 'type': 'action'},
]},
]
self.p3 = Parameter.create(name='paramsPeakFinder', type='group', \
children=self.params, expanded=True)
self.w10.setParameters(self.p3, showTop=False)
self.p3.sigTreeStateChanged.connect(self.change)
#self.parent.connect(self.launchBtn, QtCore.SIGNAL("clicked()"), self.findPeaks)
def digestRunList(self, runList):
runsToDo = []
if not runList:
print "Run(s) is empty. Please type in the run number(s)."
return runsToDo
runLists = str(runList).split(",")
for list in runLists:
temp = list.split(":")
if len(temp) == 2:
for i in np.arange(int(temp[0]),int(temp[1])+1):
runsToDo.append(i)
elif len(temp) == 1:
runsToDo.append(int(temp[0]))
return runsToDo
def updateParam(self):
if self.userUpdate is None:
if self.parent.psocakeRunDir is not None:
peakParamFname = self.parent.psocakeRunDir + '/peakParam.json'
if os.path.exists(peakParamFname):
with open(peakParamFname) as infile:
d = json.load(infile)
if d[self.hitParam_algorithm_str] == 1:
# Update variables
try:
self.hitParam_alg1_npix_min = d[self.hitParam_alg1_npix_min_str]
self.hitParam_alg1_npix_max = d[self.hitParam_alg1_npix_max_str]
self.hitParam_alg1_amax_thr = d[self.hitParam_alg1_amax_thr_str]
self.hitParam_alg1_atot_thr = d[self.hitParam_alg1_atot_thr_str]
self.hitParam_alg1_son_min = d[self.hitParam_alg1_son_min_str]
self.hitParam_alg1_thr_low = d[self.hitParam_alg1_thr_low_str]
self.hitParam_alg1_thr_high = d[self.hitParam_alg1_thr_high_str]
self.hitParam_alg1_rank = int(d[self.hitParam_alg1_rank_str])
self.hitParam_alg1_radius = int(d[self.hitParam_alg1_radius_str])
self.hitParam_alg1_dr = d[self.hitParam_alg1_dr_str]
# Update GUI
self.doingUpdate = True
#self.p3.param(self.hitParam_grp, self.hitParam_algorithm_str).setValue(self.algorithm)
self.p3.param(self.hitParam_grp, self.hitParam_algorithm1_str, self.hitParam_alg1_npix_min_str).setValue(
self.hitParam_alg1_npix_min)
self.p3.param(self.hitParam_grp, self.hitParam_algorithm1_str, self.hitParam_alg1_npix_max_str).setValue(
self.hitParam_alg1_npix_max)
self.p3.param(self.hitParam_grp, self.hitParam_algorithm1_str, self.hitParam_alg1_amax_thr_str).setValue(
self.hitParam_alg1_amax_thr)
self.p3.param(self.hitParam_grp, self.hitParam_algorithm1_str, self.hitParam_alg1_atot_thr_str).setValue(
self.hitParam_alg1_atot_thr)
self.p3.param(self.hitParam_grp, self.hitParam_algorithm1_str, self.hitParam_alg1_son_min_str).setValue(
self.hitParam_alg1_son_min)
self.p3.param(self.hitParam_grp, self.hitParam_algorithm1_str, self.hitParam_alg1_thr_low_str).setValue(
self.hitParam_alg1_thr_low)
self.p3.param(self.hitParam_grp, self.hitParam_algorithm1_str, self.hitParam_alg1_thr_high_str).setValue(
self.hitParam_alg1_thr_high)
self.p3.param(self.hitParam_grp, self.hitParam_algorithm1_str, self.hitParam_alg1_rank_str).setValue(
self.hitParam_alg1_rank)
self.p3.param(self.hitParam_grp, self.hitParam_algorithm1_str, self.hitParam_alg1_radius_str).setValue(
self.hitParam_alg1_radius)
self.doingUpdate = False
self.p3.param(self.hitParam_grp, self.hitParam_algorithm1_str, self.hitParam_alg1_dr_str).setValue(
self.hitParam_alg1_dr)
except:
pass
def writeStatus(self, fname, d):
json.dump(d, open(fname, 'w'))
# Launch peak finding
def findPeaks(self):
self.parent.thread.append(LaunchPeakFinder.LaunchPeakFinder(self.parent)) # send parent parameters with self
self.parent.thread[self.parent.threadCounter].launch(self.parent.experimentName, self.parent.detInfo)
self.parent.threadCounter+=1
# Save peak finding parameters
runsToDo = self.digestRunList(self.hitParam_runs)
for run in runsToDo:
peakParamFname = self.parent.psocakeDir+'/r'+str(run).zfill(4)+'/peakParam.json'
d = {self.hitParam_algorithm_str: self.algorithm,
self.hitParam_alg1_npix_min_str: self.hitParam_alg1_npix_min,
self.hitParam_alg1_npix_max_str: self.hitParam_alg1_npix_max,
self.hitParam_alg1_amax_thr_str: self.hitParam_alg1_amax_thr,
self.hitParam_alg1_atot_thr_str: self.hitParam_alg1_atot_thr,
self.hitParam_alg1_son_min_str: self.hitParam_alg1_son_min,
self.hitParam_alg1_thr_low_str: self.hitParam_alg1_thr_low,
self.hitParam_alg1_thr_high_str: self.hitParam_alg1_thr_high,
self.hitParam_alg1_rank_str: self.hitParam_alg1_rank,
self.hitParam_alg1_radius_str: self.hitParam_alg1_radius,
self.hitParam_alg1_dr_str: self.hitParam_alg1_dr}
self.writeStatus(peakParamFname, d)
# If anything changes in the parameter tree, print a message
def change(self, panel, changes):
for param, change, data in changes:
path = panel.childPath(param)
if self.parent.args.v >= 1:
print(' path: %s' % path)
print(' change: %s' % change)
print(' data: %s' % str(data))
print(' ----------')
self.paramUpdate(path, change, data)
##############################
# Mandatory parameter update #
##############################
def paramUpdate(self, path, change, data):
if path[0] == self.hitParam_grp:
if path[1] == self.hitParam_algorithm_str:
self.algInitDone = False
self.updateAlgorithm(data)
elif path[1] == self.hitParam_showPeaks_str:
self.showPeaks = data
self.drawPeaks()
elif path[1] == self.hitParam_outDir_str:
self.hitParam_outDir = data
self.hitParam_outDir_overridden = True
elif path[1] == self.hitParam_runs_str:
self.hitParam_runs = data
elif path[1] == self.hitParam_queue_str:
self.hitParam_queue = data
elif path[1] == self.hitParam_cpu_str:
self.hitParam_cpus = data
elif path[1] == self.hitParam_noe_str:
self.hitParam_noe = data
elif path[1] == self.hitParam_threshold_str:
self.hitParam_threshold = data
elif path[1] == self.hitParam_launch_str:
self.findPeaks()
elif path[1] == self.save_minPeaks_str:
self.minPeaks = data
elif path[1] == self.save_maxPeaks_str:
self.maxPeaks = data
elif path[1] == self.save_minRes_str:
self.minRes = data
elif path[1] == self.save_sample_str:
self.sample = data
elif path[1] == self.hitParam_extra_str:
self.hitParam_extra = data
elif path[2] == self.hitParam_alg1_npix_min_str and path[1] == self.hitParam_algorithm1_str:
self.hitParam_alg1_npix_min = data
self.algInitDone = False
self.userUpdate = True
if self.showPeaks and self.doingUpdate is False:
self.updateClassification()
elif path[2] == self.hitParam_alg1_npix_max_str and path[1] == self.hitParam_algorithm1_str:
self.hitParam_alg1_npix_max = data
self.algInitDone = False
self.userUpdate = True
if self.showPeaks and self.doingUpdate is False:
self.updateClassification()
elif path[2] == self.hitParam_alg1_amax_thr_str and path[1] == self.hitParam_algorithm1_str:
self.hitParam_alg1_amax_thr = data
self.algInitDone = False
self.userUpdate = True
if self.showPeaks and self.doingUpdate is False:
self.updateClassification()
elif path[2] == self.hitParam_alg1_atot_thr_str and path[1] == self.hitParam_algorithm1_str:
self.hitParam_alg1_atot_thr = data
self.algInitDone = False
self.userUpdate = True
if self.showPeaks and self.doingUpdate is False:
self.updateClassification()
elif path[2] == self.hitParam_alg1_son_min_str and path[1] == self.hitParam_algorithm1_str:
self.hitParam_alg1_son_min = data
self.algInitDone = False
self.userUpdate = True
if self.showPeaks and self.doingUpdate is False:
self.updateClassification()
elif path[2] == self.hitParam_alg1_thr_low_str and path[1] == self.hitParam_algorithm1_str:
self.hitParam_alg1_thr_low = data
self.algInitDone = False
self.userUpdate = True
if self.showPeaks and self.doingUpdate is False:
self.updateClassification()
elif path[2] == self.hitParam_alg1_thr_high_str and path[1] == self.hitParam_algorithm1_str:
self.hitParam_alg1_thr_high = data
self.algInitDone = False
self.userUpdate = True
if self.showPeaks and self.doingUpdate is False:
self.updateClassification()
elif path[2] == self.hitParam_alg1_rank_str and path[1] == self.hitParam_algorithm1_str:
self.hitParam_alg1_rank = data
self.algInitDone = False
self.userUpdate = True
if self.showPeaks and self.doingUpdate is False:
self.updateClassification()
elif path[2] == self.hitParam_alg1_radius_str and path[1] == self.hitParam_algorithm1_str:
self.hitParam_alg1_radius = data
self.algInitDone = False
self.userUpdate = True
if self.showPeaks and self.doingUpdate is False:
self.updateClassification()
elif path[2] == self.hitParam_alg1_dr_str and path[1] == self.hitParam_algorithm1_str:
self.hitParam_alg1_dr = data
self.algInitDone = False
self.userUpdate = True
if self.showPeaks and self.doingUpdate is False:
self.updateClassification()
def updateAlgorithm(self, data):
self.algorithm = data
self.algInitDone = False
self.updateClassification()
if self.parent.args.v >= 1: print "##### Done updateAlgorithm: ", self.algorithm
def saveCheetahFormat(self, arg):
if arg == 'lcls':
if 'cspad' in self.parent.detInfo.lower() and 'cxi' in self.parent.experimentName:
dim0 = 8 * 185
dim1 = 4 * 388
elif 'rayonix' in self.parent.detInfo.lower() and 'mfx' in self.parent.experimentName:
dim0 = 1920 #FIXME: rayonix can be binned
dim1 = 1920
elif 'rayonix' in self.parent.detInfo.lower() and 'xpp' in self.parent.experimentName:
dim0 = 1920 #FIXME: rayonix can be binned
dim1 = 1920
else:
dim0 = 0
dim1 = 0
if dim0 > 0:
maxNumPeaks = 2048
if self.parent.index.hiddenCXI is not None:
myHdf5 = h5py.File(self.parent.index.hiddenCXI, 'w')
grpName = "/entry_1/result_1"
dset_nPeaks = "/nPeaks"
dset_posX = "/peakXPosRaw"
dset_posY = "/peakYPosRaw"
dset_atot = "/peakTotalIntensity"
if grpName in myHdf5:
del myHdf5[grpName]
grp = myHdf5.create_group(grpName)
myHdf5.create_dataset(grpName + dset_nPeaks, (1,), dtype='int')
myHdf5.create_dataset(grpName + dset_posX, (1, maxNumPeaks), dtype='float32', chunks=(1, maxNumPeaks))
myHdf5.create_dataset(grpName + dset_posY, (1, maxNumPeaks), dtype='float32', chunks=(1, maxNumPeaks))
myHdf5.create_dataset(grpName + dset_atot, (1, maxNumPeaks), dtype='float32', chunks=(1, maxNumPeaks))
myHdf5.create_dataset("/LCLS/detector_1/EncoderValue", (1,), dtype=float)
myHdf5.create_dataset("/LCLS/photon_energy_eV", (1,), dtype=float)
dset = myHdf5.create_dataset("/entry_1/data_1/data", (1, dim0, dim1), dtype=float)
# Convert calib image to cheetah image
img = np.zeros((dim0, dim1))
counter = 0
if 'cspad' in self.parent.detInfo.lower() and 'cxi' in self.parent.experimentName:
for quad in range(4):
for seg in range(8):
img[seg * 185:(seg + 1) * 185, quad * 388:(quad + 1) * 388] = self.parent.calib[counter, :, :]
counter += 1
elif 'rayonix' in self.parent.detInfo.lower() and 'mfx' in self.parent.experimentName:
img = self.parent.calib[:, :] # psana format
elif 'rayonix' in self.parent.detInfo.lower() and 'xpp' in self.parent.experimentName:
img = self.parent.calib[:, :] # psana format
else:
print "saveCheetahFormat not implemented"
peaks = self.peaks.copy()
nPeaks = peaks.shape[0]
if nPeaks > maxNumPeaks:
peaks = peaks[:maxNumPeaks]
nPeaks = maxNumPeaks
for i, peak in enumerate(peaks):
seg, row, col, npix, amax, atot, rcent, ccent, rsigma, csigma, rmin, rmax, cmin, cmax, bkgd, rms, son = peak[0:17]
if 'cspad' in self.parent.detInfo.lower() and 'cxi' in self.parent.experimentName:
cheetahRow, cheetahCol = self.convert_peaks_to_cheetah(seg, row, col)
myHdf5[grpName + dset_posX][0, i] = cheetahCol
myHdf5[grpName + dset_posY][0, i] = cheetahRow
myHdf5[grpName + dset_atot][0, i] = atot
elif 'rayonix' in self.parent.detInfo.lower() and 'mfx' in self.parent.experimentName:
myHdf5[grpName + dset_posX][0, i] = col
myHdf5[grpName + dset_posY][0, i] = row
myHdf5[grpName + dset_atot][0, i] = atot
elif 'rayonix' in self.parent.detInfo.lower() and 'xpp' in self.parent.experimentName:
myHdf5[grpName + dset_posX][0, i] = col
myHdf5[grpName + dset_posY][0, i] = row
myHdf5[grpName + dset_atot][0, i] = atot
myHdf5[grpName + dset_nPeaks][0] = nPeaks
if self.parent.args.v >= 1: print "hiddenCXI clen (mm): ", self.parent.clen * 1000.
myHdf5["/LCLS/detector_1/EncoderValue"][0] = self.parent.clen * 1000. # mm
myHdf5["/LCLS/photon_energy_eV"][0] = self.parent.photonEnergy
dset[0, :, :] = img
myHdf5.close()
def updateClassification(self):
if self.parent.calib is not None:
if self.parent.mk.streakMaskOn:
self.parent.mk.initMask()
self.parent.mk.streakMask = self.parent.mk.StreakMask.getStreakMaskCalib(self.parent.evt)
if self.parent.mk.streakMask is None:
self.parent.mk.streakMaskAssem = None
else:
self.parent.mk.streakMaskAssem = self.parent.det.image(self.parent.evt, self.parent.mk.streakMask)
self.algInitDone = False
self.parent.mk.displayMask()
# update combined mask
self.parent.mk.combinedMask = np.ones_like(self.parent.calib)
if self.parent.mk.streakMask is not None and self.parent.mk.streakMaskOn is True:
self.parent.mk.combinedMask *= self.parent.mk.streakMask
if self.parent.mk.userMask is not None and self.parent.mk.userMaskOn is True:
self.parent.mk.combinedMask *= self.parent.mk.userMask
if self.parent.mk.psanaMask is not None and self.parent.mk.psanaMaskOn is True:
self.parent.mk.combinedMask *= self.parent.mk.psanaMask
# Peak output (0-16):
# 0 seg
# 1 row
# 2 col
# 3 npix: no. of pixels in the ROI intensities above threshold
# 4 amp_max: max intensity
# 5 amp_tot: sum of intensities
# 6,7: row_cgrav: center of mass
# 8,9: row_sigma
# 10,11,12,13: minimum bounding box
# 14: background
# 15: noise
# 16: signal over noise
if self.algorithm == 0: # No peak algorithm
self.peaks = None
self.drawPeaks()
else:
# Only initialize the hit finder algorithm once
if self.algInitDone is False:
self.windows = None
self.alg = []
self.alg = PyAlgos(windows=self.windows, mask=self.parent.mk.combinedMask, pbits=0)
# set peak-selector parameters:
if self.algorithm == 1:
self.alg.set_peak_selection_pars(npix_min=self.hitParam_alg1_npix_min, npix_max=self.hitParam_alg1_npix_max, \
amax_thr=self.hitParam_alg1_amax_thr, atot_thr=self.hitParam_alg1_atot_thr, \
son_min=self.hitParam_alg1_son_min)
elif self.algorithm == 2:
self.alg.set_peak_selection_pars(npix_min=self.hitParam_alg2_npix_min, npix_max=self.hitParam_alg2_npix_max, \
amax_thr=self.hitParam_alg2_amax_thr, atot_thr=self.hitParam_alg2_atot_thr, \
son_min=self.hitParam_alg2_son_min)
elif self.algorithm == 3:
self.alg.set_peak_selection_pars(npix_min=self.hitParam_alg3_npix_min, npix_max=self.hitParam_alg3_npix_max, \
amax_thr=self.hitParam_alg3_amax_thr, atot_thr=self.hitParam_alg3_atot_thr, \
son_min=self.hitParam_alg3_son_min)
elif self.algorithm == 4:
self.alg.set_peak_selection_pars(npix_min=self.hitParam_alg4_npix_min, npix_max=self.hitParam_alg4_npix_max, \
amax_thr=self.hitParam_alg4_amax_thr, atot_thr=self.hitParam_alg4_atot_thr, \
son_min=self.hitParam_alg4_son_min)
self.algInitDone = True
self.parent.calib = self.parent.calib * 1.0 # Neccessary when int is returned
if self.algorithm == 1:
# v1 - aka Droplet Finder - two-threshold peak-finding algorithm in restricted region
# around pixel with maximal intensity.
self.peakRadius = int(self.hitParam_alg1_radius)
self.peaks = self.alg.peak_finder_v4r2(self.parent.calib,
thr_low=self.hitParam_alg1_thr_low,
thr_high=self.hitParam_alg1_thr_high,
rank=int(self.hitParam_alg1_rank),
r0=self.peakRadius,
dr=self.hitParam_alg1_dr)
elif self.algorithm == 2:
# v2 - define peaks for regions of connected pixels above threshold
self.peakRadius = int(self.hitParam_alg2_r0)
self.peaks = self.alg.peak_finder_v2(self.parent.calib, thr=self.hitParam_alg2_thr, r0=self.peakRadius, dr=self.hitParam_alg2_dr)
elif self.algorithm == 3:
self.peakRadius = int(self.hitParam_alg3_r0)
self.peaks = self.alg.peak_finder_v3(self.parent.calib, rank=self.hitParam_alg3_rank, r0=self.peakRadius, dr=self.hitParam_alg3_dr)
elif self.algorithm == 4:
# v4 - aka Droplet Finder - the same as v1, but uses rank and r0 parameters in stead of common radius.
self.peakRadius = int(self.hitParam_alg4_r0)
self.peaks = self.alg.peak_finder_v4(self.parent.calib, thr_low=self.hitParam_alg4_thr_low, thr_high=self.hitParam_alg4_thr_high,
rank=self.hitParam_alg4_rank, r0=self.peakRadius, dr=self.hitParam_alg4_dr)
self.numPeaksFound = self.peaks.shape[0]
if self.parent.args.v >= 1: print "Num peaks found: ", self.numPeaksFound, self.peaks.shape
# update clen
self.parent.geom.updateClen('lcls')
self.parent.index.clearIndexedPeaks()
# Save image and peaks in cheetah cxi file
self.saveCheetahFormat('lcls')
if self.parent.index.showIndexedPeaks: self.parent.index.updateIndex()
self.drawPeaks()
if self.parent.args.v >= 1: print "Done updateClassification"
def convert_peaks_to_cheetah(self, s, r, c) :
"""Converts seg, row, col assuming (32,185,388)
to cheetah 2-d table row and col (8*185, 4*388)
"""
segs, rows, cols = (32,185,388)
row2d = (int(s)%8) * rows + int(r) # where s%8 is a segment in quad number [0,7]
col2d = (int(s)/8) * cols + int(c) # where s/8 is a quad number [0,3]
return row2d, col2d
def getMaxRes(self, posX, posY, centerX, centerY):
maxRes = np.max(np.sqrt((posX-centerX)**2 + (posY-centerY)**2))
if self.parent.args.v >= 1: print "maxRes: ", maxRes
return maxRes # in pixels
def drawPeaks(self):
self.parent.img.clearPeakMessage()
if self.showPeaks:
if self.peaks is not None and self.numPeaksFound > 0:
self.ix = self.parent.det.indexes_x(self.parent.evt)
self.iy = self.parent.det.indexes_y(self.parent.evt)
if self.ix is None:
(_, dim0, dim1) = self.parent.calib.shape
self.iy = np.tile(np.arange(dim0),[dim1, 1])
self.ix = np.transpose(self.iy)
self.iX = np.array(self.ix, dtype=np.int64)
self.iY = np.array(self.iy, dtype=np.int64)
if len(self.iX.shape)==2:
self.iX = np.expand_dims(self.iX,axis=0)
self.iY = np.expand_dims(self.iY,axis=0)
cenX = self.iX[np.array(self.peaks[:,0],dtype=np.int64),np.array(self.peaks[:,1],dtype=np.int64),np.array(self.peaks[:,2],dtype=np.int64)] + 0.5
cenY = self.iY[np.array(self.peaks[:,0],dtype=np.int64),np.array(self.peaks[:,1],dtype=np.int64),np.array(self.peaks[:,2],dtype=np.int64)] + 0.5
self.peaksMaxRes = self.getMaxRes(cenX, cenY, self.parent.cx, self.parent.cy)
diameter = self.peakRadius*2+1
self.parent.img.peak_feature.setData(cenX, cenY, symbol='s', \
size=diameter, brush=(255,255,255,0), \
pen=pg.mkPen({'color': "c", 'width': 4}), pxMode=False) #FF0
# Write number of peaks found
xMargin = 5 # pixels
yMargin = 0 # pixels
maxX = np.max(self.ix) + xMargin
maxY = np.max(self.iy) - yMargin
myMessage = '<div style="text-align: center"><span style="color: cyan; font-size: 12pt;">Peaks=' + \
str(self.numPeaksFound) + ' <br>Res=' + str(int(self.peaksMaxRes)) + '<br></span></div>'
self.parent.img.peak_text = pg.TextItem(html=myMessage, anchor=(0, 0))
self.parent.img.w1.getView().addItem(self.parent.img.peak_text)
self.parent.img.peak_text.setPos(maxX, maxY)
else:
self.parent.img.peak_feature.setData([], [], pxMode=False)
self.parent.img.peak_text = pg.TextItem(html='', anchor=(0, 0))
self.parent.img.w1.getView().addItem(self.parent.img.peak_text)
self.parent.img.peak_text.setPos(0,0)
else:
self.parent.img.peak_feature.setData([], [], pxMode=False)
if self.parent.args.v >= 1: print "Done updatePeaks"
class TofGate():
def __init__(self, parent, gateName, tofs, tofl):
self.parent = parent
self.gateName = gateName
self.tofs = tofs
self.tofl = tofl
self.tofbin = self.parent.monitor_params['t']['bin']
self.tof_ind = (self.parent.TaxisM>self.tofs) & (self.parent.TaxisM<self.tofl)
self.particle = self.gateName
self.extractE = extractE
self.alg = PyAlgos()
self.alg.set_peak_selection_pars(npix_min=self.parent.npix_min, npix_max=self.parent.npix_max, amax_thr=self.parent.amax_thr, atot_thr=self.parent.atot_thr, son_min=self.parent.son_min)
self.init_vars()
def init_vars(self):
self.num_events = 0
self.P = 0
self.temp_irun = -1
self.tempXef1 = []
self.tempYef1 = []
self.tempXef2 = []
self.tempYef2 = []
self.tempErf1 = []
self.tempErf2 = []
self.hists_tof = {}
self.hists_tof_all = {}
for hist_name in self.parent.hist_names_tof:
x_name = self.parent.monitor_params[hist_name]['x']
x_binnum = self.parent.vars_binnum[x_name]
if 'y' in self.parent.monitor_params[hist_name].keys():
y_name = self.parent.monitor_params[hist_name]['y']
y_binnum = self.parent.vars_binnum[y_name]
if 'z' in self.parent.monitor_params[hist_name].keys():
z_name = self.parent.monitor_params[hist_name]['z']
z_binnum = self.parent.vars_binnum[z_name]
self.hists_tof[hist_name] = np.zeros([x_binnum-1, y_binnum-1, z_binnum-1])
if self.parent.role == 'master':
self.hists_tof_all[hist_name] = np.zeros([x_binnum-1, y_binnum-1, z_binnum-1])
else:
self.hists_tof_all[hist_name] = None
else:
self.hists_tof[hist_name] = np.zeros([x_binnum-1, y_binnum-1])
if self.parent.role == 'master':
self.hists_tof_all[hist_name] = np.zeros([x_binnum-1, y_binnum-1])
else:
self.hists_tof_all[hist_name] = None
else:
self.hists_tof[hist_name] = np.zeros([x_binnum-1])
if self.parent.role == 'master':
self.hists_tof_all[hist_name] = np.zeros([x_binnum-1])
else:
self.hists_tof_all[hist_name] = None
def update_electron(self, eAind, eRind, eXind, eYind):
# self.eA[eAind,0] += 1
# self.eR[eRind,0] += 1
self.hists_tof['ex_ey_tg'][eXind, eYind] += 1
# self.eAR[eAind, eRind] += 1
def update_ion(self, Tofind, Xind, Yind, Tofele, Xele, Yele):
if (self.parent.TaxisM[Tofind] > self.tofs and self.parent.TaxisM[Tofind] < self.tofl):
self.P += 1
self.hists_tof['x_y_tg'][Xind, Yind] += 1
# self.XT[Tofind, Xind] += 1
# self.YT[Tofind, Yind] += 1
def is_coin(self):
return self.P > 0
def update_shotinfo(self, pho_ind, pls_ind):
self.hists_tof['pho_pls_eng_tg'][pho_ind, pls_ind] += 1
self.num_events += 1
temp_num_particles_ind = next((tNP_ind for tNP_ind, tNP_ele in enumerate(self.parent.vars_axis['num_particles']) if tNP_ele > self.P),0)-1
if temp_num_particles_ind != -1:
self.hists_tof['num_particles_tg'][temp_num_particles_ind] += 1
self.tempXef1, self.tempYef1,self.tempErf1,self.pho_ind_f1,self.pls_ind_f1 = self.processDataE(self.parent.irun,self.parent.ievt-1)
self.tempXef2, self.tempYef2,self.tempErf2,self.pho_ind_f2,self.pls_ind_f2 = self.processDataE(self.parent.irun,self.parent.ievt+1)
if len(self.tempXef1) > 0:
for itemp in range(len(self.tempXef1)):
self.hists_tof['ex_ey_tg_fls1'][self.tempXef1[itemp],self.tempYef1[itemp]] += 1
self.hists_tof['pho_pls_eng_tg_fls1'][self.pho_ind_f1, self.pls_ind_f1] += 1
if len(self.tempXef2) > 0:
for itemp in range(len(self.tempXef2)):
self.hists_tof['ex_ey_tg_fls2'][self.tempXef2[itemp],self.tempYef2[itemp]] += 1
self.hists_tof['pho_pls_eng_tg_fls2'][self.pho_ind_f2, self.pls_ind_f2] += 1
def reset_coin_var(self):
self.P = 0
def save_var(self, h5f):
grp = h5f.create_group('TofGate_'+self.gateName)
grp.create_dataset('gateInfo_tofs_tofl_tofbin', data=np.array([self.tofs,self.tofl,self.tofbin]))
for histname in self.parent.hist_names_tof:
try:
grp.create_dataset(histname,data = self.hists_tof_all[histname])
except Exception as e:
print(histname+' failed to be saved.')
print(e)
def reduce(self):
for histname in self.parent.hist_names_tof:
try:
self.time_a = time.time()
MPI.COMM_WORLD.Reduce(self.hists_tof[histname],self.hists_tof_all[histname])
self.time_b = time.time()
print(histname+' reduced by '+'Rank {0} in {1:.2f} seconds'.format(self.parent.mpi_rank,self.time_b - self.time_a))
except Exception as e:
print(histname+' failed to be redueced by '+str(self.parent.mpi_rank))
print(e)
def processDataE(self,irun,ievt):
if self.temp_irun != irun:
self.psana_source = psana.DataSource(self.parent.source)
for ii, rr in enumerate(self.psana_source.runs()):
if ii==irun:
self.temp_irun = irun
self.temprun = rr
break
self.temptimes = self.temprun.times()
self.temp_len_run = len(self.temptimes)
if ievt<0 or ievt>=self.temp_len_run:
return [], [], [],None,None
event = {'evt': self.temprun.event(self.temptimes[ievt])}
if event['evt'] is None:
return [], [], [],None,None
self.extractE(event,self)
if self.eImage_f is None or self.pulse_eng_f is None or self.photon_eng_f is None:
return [], [], [],None, None
photon_eng_ind = next((pho_eng_ind for pho_eng_ind, pho_eng_ele in enumerate(self.parent.vars_axis['phoeng']) if pho_eng_ele > self.photon_eng_f),0)-1
pulse_eng_ind = next((pls_eng_ind for pls_eng_ind, pls_eng_ele in enumerate(self.parent.vars_axis['plseng']) if pls_eng_ele > self.pulse_eng_f),0)-1
if photon_eng_ind ==-1 or pulse_eng_ind == -1:
return [], [], [],None, None
eXinds = []
eYinds = []
eRinds = []
e_peaks = self.alg.peak_finder_v4r2(self.eImage_f, thr_low=self.parent.thr_low, thr_high=self.parent.thr_high, rank=self.parent.rank, r0=self.parent.r0, dr=self.parent.dr)
eXs = e_peaks[:,1].astype(int)
eYs = e_peaks[:,2].astype(int)
eRadius, eAngle =self.parent.cart2polar(eXs, eYs)
for e_ind in range(len(eXs)):
tempRind = next((eR_ind for eR_ind, eR_ele in enumerate(self.parent.vars_axis['er']) if eR_ele > eRadius[e_ind]),0)-1
# tempAind = next((eA_ind for eA_ind, eA_ele in enumerate(self.eAaxis) if eA_ele > eAngle[e_ind]),0)
tempXind = next((eX_ind for eX_ind, eX_ele in enumerate(self.parent.vars_axis['ex']) if eX_ele > eXs[e_ind]),0)-1
tempYind = next((eY_ind for eY_ind, eY_ele in enumerate(self.parent.vars_axis['ey']) if eY_ele > eYs[e_ind]),0)-1
# if tempRind != 0 and tempAind != 0 and tempXind != 0 and tempYind != 0:
if tempXind != -1 and tempYind != -1:
eXinds.append(tempXind)
eYinds.append(tempYind)
eRinds.append(tempRind)
if len(eXinds) > 0:
return eXinds, eYinds, eRinds, photon_eng_ind, pulse_eng_ind
else:
return [], [],[],None,None
class PiPiCoGate():
def __init__(self, parent, gateName, tof1s, tof1l, tof2s, tof2l, thresh1,
thresh2, ang_f):
self.parent = parent
self.gateName = gateName
self.tof1s = tof1s
self.tof1l = tof1l
self.tof2s = tof2s
self.tof2l = tof2l
self.extractE = extractE
self.PiPiCo_ind1 = (self.parent.TaxisM >
self.tof1s) & (self.parent.TaxisM < self.tof1l)
self.PiPiCo_ind2 = (self.parent.TaxisM >
self.tof2s) & (self.parent.TaxisM < self.tof2l)
self.thresh1 = thresh1
self.thresh2 = thresh2
self.ang_f = ang_f
self.cos_theta_f1 = np.cos(0.5 * self.ang_f * np.pi / 180)
self.cos_theta_f2 = np.cos((90 - 0.5 * self.ang_f) * np.pi / 180)
self.cos_theta_f3 = -np.cos((90 - 0.5 * self.ang_f) * np.pi / 180)
self.cos_theta_f4 = -np.cos(0.5 * self.ang_f * np.pi / 180)
self.particle1 = self.gateName[:2]
self.particle2 = self.gateName[2:4]
self.alg = PyAlgos()
self.alg.set_peak_selection_pars(npix_min=self.parent.npix_min,
npix_max=self.parent.npix_max,
amax_thr=self.parent.amax_thr,
atot_thr=self.parent.atot_thr,
son_min=self.parent.son_min)
self.init_vars()
def init_vars(self):
self.temp_len_run = -1
self.temp_irun = -1
self.num_events = 0
self.num_eventss = 0
self.num_eventsl = 0
self.num_eventsm = 0
self.num_events_orth = 0
self.num_events_para = 0
self.num_events_f = 0
self.num_eventss_f = 0
self.num_eventsl_f = 0
self.num_eventsm_f = 0
self.num_events_orth_f = 0
self.num_events_para_f = 0
self.P1 = 0
self.P2 = 0
self.P12 = 0
self.Ps = 0
self.Pl = 0
self.Pm = 0
self.P_orth = 0
self.P_para = 0
# self.XY = np.zeros([self.parent.Xbinnum-1, self.parent.Ybinnum-1])
# self.XT = np.zeros([self.parent.Tbinnum-1, self.parent.Xbinnum-1])
# self.YT = np.zeros([self.parent.Tbinnum-1, self.parent.Ybinnum-1])
# self.PiPiCo = np.zeros([self.parent.Tbinnum-2,self.parent.Tbinnum-2])
self.tempX1ind = []
self.tempY1ind = []
self.tempX2ind = []
self.tempY2ind = []
self.tempX1 = []
self.tempY1 = []
self.tempT1 = []
self.tempT1_f = []
self.tempX2 = []
self.tempY2 = []
self.tempT2 = []
self.tempT2_f = []
self.tempXe = []
self.tempYe = []
self.tempXef1 = []
self.tempYef1 = []
self.tempXef2 = []
self.tempYef2 = []
self.tempEr = []
self.tempErf1 = []
self.tempErf2 = []
self.tempEng1_arr = []
self.tempPx1_arr = []
self.tempPy1_arr = []
self.tempPz1_arr = []
self.tempEng2_arr = []
self.tempPx2_arr = []
self.tempPy2_arr = []
self.tempPz2_arr = []
self.tempEng1ind_arr = []
self.tempPx1ind_arr = []
self.tempPy1ind_arr = []
self.tempPz1ind_arr = []
self.tempEng2ind_arr = []
self.tempPx2ind_arr = []
self.tempPy2ind_arr = []
self.tempPz2ind_arr = []
self.tempX1ind_arr = []
self.tempY1ind_arr = []
self.tempX2ind_arr = []
self.tempY2ind_arr = []
self.hists_pipico = {}
self.hists_pipico_all = {}
for hist_name in self.parent.hist_names_pipico:
if hist_name == 'pipico':
x_name = self.parent.monitor_params[hist_name]['x']
x_bin = self.parent.monitor_params[x_name]['bin']
y_name = self.parent.monitor_params[hist_name]['y']
y_bin = self.parent.monitor_params[y_name]['bin']
self.pipico_xbin = x_bin
self.pipico_ybin = y_bin
x_binnum = int((self.tof1l - self.tof1s) / x_bin) + 1
y_binnum = int((self.tof2l - self.tof2s) / y_bin) + 1
self.pipico_xaxis = np.linspace(self.tof1s, self.tof1l,
x_binnum)
self.pipico_yaxis = np.linspace(self.tof2s, self.tof2l,
y_binnum)
self.hists_pipico[hist_name] = np.zeros(
[x_binnum - 1, y_binnum - 1])
if self.parent.role == 'master':
self.hists_pipico_all[hist_name] = np.zeros(
[x_binnum - 1, y_binnum - 1])
else:
self.hists_pipico_all[hist_name] = None
continue
x_name = self.parent.monitor_params[hist_name]['x']
x_binnum = self.parent.vars_binnum[x_name]
if 'y' in self.parent.monitor_params[hist_name].keys():
y_name = self.parent.monitor_params[hist_name]['y']
y_binnum = self.parent.vars_binnum[y_name]
if 'z' in self.parent.monitor_params[hist_name].keys():
z_name = self.parent.monitor_params[hist_name]['z']
z_binnum = self.parent.vars_binnum[z_name]
self.hists_pipico[hist_name] = np.zeros(
[x_binnum - 1, y_binnum - 1, z_binnum - 1])
if self.parent.role == 'master':
self.hists_pipico_all[hist_name] = np.zeros(
[x_binnum - 1, y_binnum - 1, z_binnum - 1])
else:
self.hists_pipico_all[hist_name] = None
else:
self.hists_pipico[hist_name] = np.zeros(
[x_binnum - 1, y_binnum - 1])
if self.parent.role == 'master':
self.hists_pipico_all[hist_name] = np.zeros(
[x_binnum - 1, y_binnum - 1])
else:
self.hists_pipico_all[hist_name] = None
else:
self.hists_pipico[hist_name] = np.zeros([x_binnum - 1])
if self.parent.role == 'master':
self.hists_pipico_all[hist_name] = np.zeros([x_binnum - 1])
else:
self.hists_pipico_all[hist_name] = None
def update_ion(self, Tofind, Xind, Yind, Tofele, Xele, Yele):
if (self.parent.TaxisM[Tofind] > self.tof1s
and self.parent.TaxisM[Tofind] < self.tof1l):
self.P1 += 1
# self.tempX1ind.append(Xind)
# self.tempY1ind.append(Yind)
self.tempX1.append(Xele)
self.tempY1.append(Yele)
self.tempT1.append(Tofele)
if (self.parent.TaxisM[Tofind] > self.tof2s
and self.parent.TaxisM[Tofind] < self.tof2l):
self.P2 += 1
# self.tempX2ind.append(Xind)
# self.tempY2ind.append(Yind)
self.tempX2.append(Xele)
self.tempY2.append(Yele)
self.tempT2.append(Tofele)
if (self.parent.TaxisM[Tofind] > self.tof1s
and self.parent.TaxisM[Tofind] < self.tof1l) and (
self.parent.TaxisM[Tofind] > self.tof2s
and self.parent.TaxisM[Tofind] < self.tof2l):
self.P12 += 1
def is_coin(self):
return (self.P1 > 0 and self.P2 > 0 and self.P12 == 0) or self.P12 > 1
def update_electron(self, eAind, eRind, eXind, eYind, pho_ind, pls_ind):
self.tempXe.append(eXind)
self.tempYe.append(eYind)
self.tempEr.append(eRind)
# def update_ion(self, Tofind, Xind, Yind):
#
# self.XY[Xind, Yind] += 1
# self.XT[Tind, Xind] += 1
# self.YT[Tind, Yind] += 1
def update_shotinfo(self, pho_ind, pls_ind, pho_eng, pls_eng):
temp_num_particles_ind1 = next(
(tNP_ind1 for tNP_ind1, tNP_ele1 in enumerate(
self.parent.vars_axis['num_particles']) if tNP_ele1 > self.P1),
0) - 1
temp_num_particles_ind2 = next(
(tNP_ind2 for tNP_ind2, tNP_ele2 in enumerate(
self.parent.vars_axis['num_particles']) if tNP_ele2 > self.P2),
0) - 1
if temp_num_particles_ind1 != -1:
self.hists_pipico['num_particles1_'][temp_num_particles_ind1] += 1
if temp_num_particles_ind2 != -1:
self.hists_pipico['num_particles2_'][temp_num_particles_ind2] += 1
for itemp1 in range(len(self.tempX1)):
tempEng1, tempPx1, tempPy1, tempPz1 = EngMo(
self.tempT1[itemp1], self.tempX1[itemp1], self.tempY1[itemp1],
self.parent.tsim[self.particle1], self.parent.xcenter,
self.parent.ycenter, self.parent.toff[self.particle1],
self.particle1)
tempEng_ind1 = next(
(tEng_ind1 for tEng_ind1, tEng_ele1 in enumerate(
self.parent.vars_axis['eng']) if tEng_ele1 > tempEng1),
0) - 1
tempPx_ind1 = next((tPx_ind1 for tPx_ind1, tPx_ele1 in enumerate(
self.parent.vars_axis['px']) if tPx_ele1 > tempPx1), 0) - 1
tempPy_ind1 = next((tPy_ind1 for tPy_ind1, tPy_ele1 in enumerate(
self.parent.vars_axis['py']) if tPy_ele1 > tempPy1), 0) - 1
tempPz_ind1 = next((tPz_ind1 for tPz_ind1, tPz_ele1 in enumerate(
self.parent.vars_axis['pz']) if tPz_ele1 > tempPz1), 0) - 1
if tempEng_ind1 != -1 and tempPx_ind1 != -1 and tempPy_ind1 != -1 and tempPz_ind1 != -1:
# self.Eng1[tempEng_ind1-1] += 1
# self.Momen1[tempPx_ind1-1, tempPy_ind1-1, tempPz_ind1-1] += 1
self.tempEng1_arr.append(tempEng1)
self.tempPx1_arr.append(tempPx1)
self.tempPy1_arr.append(tempPy1)
self.tempPz1_arr.append(tempPz1)
self.tempEng1ind_arr.append(tempEng_ind1)
self.tempPx1ind_arr.append(tempPx_ind1)
self.tempPy1ind_arr.append(tempPy_ind1)
self.tempPz1ind_arr.append(tempPz_ind1)
self.tempT1_f.append(self.tempT1[itemp1])
# self.tempX1ind_arr.append(self.tempX1ind[itemp1])
# self.tempY1ind_arr.append(self.tempY1ind[itemp1])
for itemp2 in range(len(self.tempX2)):
tempEng2, tempPx2, tempPy2, tempPz2 = EngMo(
self.tempT2[itemp2], self.tempX2[itemp2], self.tempY2[itemp2],
self.parent.tsim[self.particle2], self.parent.xcenter,
self.parent.ycenter, self.parent.toff[self.particle2],
self.particle2)
tempEng_ind2 = next(
(tEng_ind2 for tEng_ind2, tEng_ele2 in enumerate(
self.parent.vars_axis['eng']) if tEng_ele2 > tempEng2),
0) - 1
tempPx_ind2 = next((tPx_ind2 for tPx_ind2, tPx_ele2 in enumerate(
self.parent.vars_axis['px']) if tPx_ele2 > tempPx2), 0) - 1
tempPy_ind2 = next((tPy_ind2 for tPy_ind2, tPy_ele2 in enumerate(
self.parent.vars_axis['py']) if tPy_ele2 > tempPy2), 0) - 1
tempPz_ind2 = next((tPz_ind2 for tPz_ind2, tPz_ele2 in enumerate(
self.parent.vars_axis['pz']) if tPz_ele2 > tempPz2), 0) - 1
if tempEng_ind2 != -1 and tempPx_ind2 != -1 and tempPy_ind2 != -1 and tempPz_ind2 != -1:
# self.Eng2[tempEng_ind2-1] += 1
# self.Momen2[tempPx_ind2-1, tempPy_ind2-1, tempPz_ind2-1] += 1
self.tempEng2_arr.append(tempEng2)
self.tempPx2_arr.append(tempPx2)
self.tempPy2_arr.append(tempPy2)
self.tempPz2_arr.append(tempPz2)
self.tempEng2ind_arr.append(tempEng_ind2)
self.tempPx2ind_arr.append(tempPx_ind2)
self.tempPy2ind_arr.append(tempPy_ind2)
self.tempPz2ind_arr.append(tempPz_ind2)
self.tempT2_f.append(self.tempT2[itemp2])
# self.tempX2ind_arr.append(self.tempX2ind[itemp2])
# self.tempY2ind_arr.append(self.tempY2ind[itemp2])
for i1 in range(len(self.tempEng1_arr)):
for i2 in range(len(self.tempEng2_arr)):
tempEng = self.tempEng1_arr[i1] + self.tempEng2_arr[i2]
tempPx = self.tempPx1_arr[i1] + self.tempPx2_arr[i2]
tempPy = self.tempPy1_arr[i1] + self.tempPy2_arr[i2]
tempPz = self.tempPz1_arr[i1] + self.tempPz2_arr[i2]
if np.abs(tempPx) > self.parent.pxf or np.abs(
tempPy) > self.parent.pyf or np.abs(
tempPz) > self.parent.pzf or tempEng < 1:
continue
tempT1_ind = next(
(t1_ind for t1_ind, t1_ele in enumerate(self.pipico_xaxis)
if t1_ele > self.tempT1_f[i1]), 0) - 1
tempT2_ind = next(
(t2_ind for t2_ind, t2_ele in enumerate(self.pipico_yaxis)
if t2_ele > self.tempT2_f[i2]), 0) - 1
if tempT1_ind != -1 and tempT2_ind != -1:
self.hists_pipico['pipico'][tempT1_ind, tempT2_ind] += 1
tempEng_ind = next(
(tEng_ind for tEng_ind, tEng_ele in enumerate(
self.parent.vars_axis['eng']) if tEng_ele > tempEng),
0) - 1
tempEng_a_ind = next(
(tEng_ind for tEng_ind, tEng_ele in enumerate(
self.parent.vars_axis['eng_a']) if tEng_ele > tempEng),
0) - 1
tempPx_ind = next((tPx_ind for tPx_ind, tPx_ele in enumerate(
self.parent.vars_axis['px']) if tPx_ele > tempPx), 0) - 1
tempPy_ind = next((tPy_ind for tPy_ind, tPy_ele in enumerate(
self.parent.vars_axis['py']) if tPy_ele > tempPy), 0) - 1
tempPz_ind = next((tPz_ind for tPz_ind, tPz_ele in enumerate(
self.parent.vars_axis['pz']) if tPz_ele > tempPz), 0) - 1
# tempPx1_ind = next((tPx1_ind for tPx1_ind, tPx1_ele in enumerate(self.parent.PXaxis) if tPx1_ele > tempPx1[i1]),0)
# tempPy1_ind = next((tPy1_ind for tPy1_ind, tPy1_ele in enumerate(self.parent.PYaxis) if tPy1_ele > tempPy1[i1]),0)
# tempPz1_ind = next((tPz1_ind for tPz1_ind, tPz1_ele in enumerate(self.parent.PZaxis) if tPz1_ele > tempPz1[i1]),0)
if tempEng_ind != -1 and tempPx_ind != -1 and tempPy_ind != -1 and tempPz_ind != -1:
self.hists_pipico['eng_'][tempEng_ind] += 1
self.hists_pipico['eng_a_'][tempEng_a_ind] += 1
self.hists_pipico['eng_a_pho_'][tempEng_a_ind] += pho_eng
self.hists_pipico['eng_a_pls_'][tempEng_a_ind] += pls_eng
self.hists_pipico['eng_a_er'][tempEng_a_ind,
self.tempEr] += 1
# self.Momen[tempPx_ind-1, tempPy_ind-1, tempPz_ind-1] += 1
self.hists_pipico['pxsum_'][tempPx_ind] += 1
self.hists_pipico['pysum_'][tempPy_ind] += 1
self.hists_pipico['pzsum_'][tempPz_ind] += 1
# self.Momenf1[self.tempPx1ind_arr[i1], self.tempPy1ind_arr[i1], self.tempPz1ind_arr[i1]] += 1
# self.Momenf2[self.tempPx2ind_arr[i2], self.tempPy2ind_arr[i2], self.tempPz2ind_arr[i2]] += 1
# self.X1Y1[self.tempX1ind_arr[i1],self.tempY1ind_arr[i1]] += 1
# self.X2Y2[self.tempX2ind_arr[i2],self.tempY2ind_arr[i2]] += 1
cos_theta = tempPx / np.sqrt(tempPx**2 + tempPy**2 +
tempPz**2)
cos_theta_orth = tempPy / np.sqrt(tempPx**2 + tempPy**2 +
tempPz**2)
if cos_theta >= self.cos_theta_f1 or cos_theta <= self.cos_theta_f4:
self.hists_pipico['eng_para_'][tempEng_ind] += 1
# self.Eng1_para[self.tempEng1ind_arr[i1]] += 1
# self.Eng2_para[self.tempEng2ind_arr[i2]] += 1
# self.Momens1[self.tempPx1ind_arr[i1], self.tempPy1ind_arr[i1], self.tempPz1ind_arr[i1]] += 1
# self.Momens2[self.tempPx2ind_arr[i2], self.tempPy2ind_arr[i2], self.tempPz2ind_arr[i2]] += 1
self.P_para += 1
# elif cos_theta >= self.cos_theta_f3 and cos_theta <= self.cos_theta_f2:
# self.Eng_orth[tempEng_ind] += 1
elif cos_theta_orth >= self.cos_theta_f1 or cos_theta_orth <= self.cos_theta_f4:
self.hists_pipico['eng_orth_'][tempEng_ind] += 1
# self.Eng1_orth[self.tempEng1ind_arr[i1]] += 1
# self.Eng2_orth[self.tempEng2ind_arr[i2]] += 1
# self.Momenl1[self.tempPx1ind_arr[i1], self.tempPy1ind_arr[i1], self.tempPz1ind_arr[i1]] += 1
# self.Momenl2[self.tempPx2ind_arr[i2], self.tempPy2ind_arr[i2], self.tempPz2ind_arr[i2]] += 1
# self.Momenl[tempPx_ind, tempPy_ind, tempPz_ind] += 1
self.P_orth += 1
if tempEng < self.thresh1:
self.hists_pipico['eng_s_'][tempEng_ind] += 1
# self.Engs1[self.tempEng1ind_arr[i1]-1] += 1
# self.Engs2[self.tempEng2ind_arr[i2]-1] += 1
# self.Momens1[self.tempPx1ind_arr[i1]-1, self.tempPy1ind_arr[i1]-1, self.tempPz1ind_arr[i1]-1] += 1
# self.Momens2[self.tempPx2ind_arr[i2]-1, self.tempPy2ind_arr[i2]-1, self.tempPz2ind_arr[i2]-1] += 1
self.Ps += 1
elif tempEng >= self.thresh1 and tempEng < self.thresh2:
self.hists_pipico['eng_m_'][tempEng_ind] += 1
# self.Engm1[self.tempEng1ind_arr[i1]-1] += 1
# self.Engm2[self.tempEng2ind_arr[i2]-1] += 1
# self.Momenm1[self.tempPx1ind_arr[i1]-1, self.tempPy1ind_arr[i1]-1, self.tempPz1ind_arr[i1]-1] += 1
# self.Momenm2[self.tempPx2ind_arr[i2]-1, self.tempPy2ind_arr[i2]-1, self.tempPz2ind_arr[i2]-1] += 1
# self.Momenl[tempPx_ind-1, tempPy_ind-1, tempPz_ind-1] += 1
self.Pm += 1
elif tempEng >= self.thresh2:
self.hists_pipico['eng_l_'][tempEng_ind] += 1
# self.Engl1[self.tempEng1ind_arr[i1]-1] += 1
# self.Engl2[self.tempEng2ind_arr[i2]-1] += 1
# self.Momenl1[self.tempPx1ind_arr[i1]-1, self.tempPy1ind_arr[i1]-1, self.tempPz1ind_arr[i1]-1] += 1
# self.Momenl2[self.tempPx2ind_arr[i2]-1, self.tempPy2ind_arr[i2]-1, self.tempPz2ind_arr[i2]-1] += 1
# self.Momenl[tempPx_ind-1, tempPy_ind-1, tempPz_ind-1] += 1
self.Pl += 1
self.tempXef1, self.tempYef1, self.tempErf1, self.pho_ind_f1, self.pls_ind_f1 = self.processDataE(
self.parent.irun, self.parent.ievt - 1)
self.tempXef2, self.tempYef2, self.tempErf2, self.pho_ind_f2, self.pls_ind_f2 = self.processDataE(
self.parent.irun, self.parent.ievt + 1)
if self.Ps > 0:
self.fill_ele(self.tempXe, self.tempYe, self.tempEr, pho_ind,
pls_ind, self.hists_pipico['ex_ey_s'],
self.hists_pipico['er_er_s'],
self.hists_pipico['pho_pls_eng_s'], self.num_eventss)
self.fill_ele_f(self.tempXef1, self.tempYef1, self.tempErf1,
self.pho_ind_f1, self.pls_ind_f1,
self.hists_pipico['ex_ey_s_fls1'],
self.hists_pipico['er_er_s_fls1'],
self.hists_pipico['pho_pls_eng_s_fls1'],
self.num_eventss_f)
self.fill_ele_f(self.tempXef2, self.tempYef2, self.tempErf2,
self.pho_ind_f2, self.pls_ind_f2,
self.hists_pipico['ex_ey_s_fls2'],
self.hists_pipico['er_er_s_fls2'],
self.hists_pipico['pho_pls_eng_s_fls2'],
self.num_eventss_f)
if self.Pm > 0:
self.fill_ele(self.tempXe, self.tempYe, self.tempEr, pho_ind,
pls_ind, self.hists_pipico['ex_ey_m'],
self.hists_pipico['er_er_m'],
self.hists_pipico['pho_pls_eng_m'], self.num_eventsm)
self.fill_ele_f(self.tempXef1, self.tempYef1, self.tempErf1,
self.pho_ind_f1, self.pls_ind_f1,
self.hists_pipico['ex_ey_m_fls1'],
self.hists_pipico['er_er_m_fls1'],
self.hists_pipico['pho_pls_eng_m_fls1'],
self.num_eventsm_f)
self.fill_ele_f(self.tempXef2, self.tempYef2, self.tempErf2,
self.pho_ind_f2, self.pls_ind_f2,
self.hists_pipico['ex_ey_m_fls2'],
self.hists_pipico['er_er_m_fls2'],
self.hists_pipico['pho_pls_eng_m_fls2'],
self.num_eventsm_f)
if self.Pl > 0:
self.fill_ele(self.tempXe, self.tempYe, self.tempEr, pho_ind,
pls_ind, self.hists_pipico['ex_ey_l'],
self.hists_pipico['er_er_l'],
self.hists_pipico['pho_pls_eng_l'], self.num_eventsl)
self.fill_ele_f(self.tempXef1, self.tempYef1, self.tempErf1,
self.pho_ind_f1, self.pls_ind_f1,
self.hists_pipico['ex_ey_l_fls1'],
self.hists_pipico['er_er_l_fls1'],
self.hists_pipico['pho_pls_eng_l_fls1'],
self.num_eventsl_f)
self.fill_ele_f(self.tempXef2, self.tempYef2, self.tempErf2,
self.pho_ind_f2, self.pls_ind_f2,
self.hists_pipico['ex_ey_l_fls2'],
self.hists_pipico['er_er_l_fls2'],
self.hists_pipico['pho_pls_eng_l_fls2'],
self.num_eventsl_f)
if self.P_para > 0:
self.fill_ele(self.tempXe, self.tempYe, self.tempEr, pho_ind,
pls_ind, self.hists_pipico['ex_ey_para'],
self.hists_pipico['er_er_para'],
self.hists_pipico['pho_pls_eng_para'],
self.num_events_para)
self.fill_ele_f(self.tempXef1, self.tempYef1, self.tempErf1,
self.pho_ind_f1, self.pls_ind_f1,
self.hists_pipico['ex_ey_para_fls1'],
self.hists_pipico['er_er_para_fls1'],
self.hists_pipico['pho_pls_eng_para_fls1'],
self.num_events_para_f)
self.fill_ele_f(self.tempXef2, self.tempYef2, self.tempErf2,
self.pho_ind_f2, self.pls_ind_f2,
self.hists_pipico['ex_ey_para_fls2'],
self.hists_pipico['er_er_para_fls2'],
self.hists_pipico['pho_pls_eng_para_fls2'],
self.num_events_para_f)
if self.P_orth > 0:
self.fill_ele(self.tempXe, self.tempYe, self.tempEr, pho_ind,
pls_ind, self.hists_pipico['ex_ey_orth'],
self.hists_pipico['er_er_orth'],
self.hists_pipico['pho_pls_eng_orth'],
self.num_events_orth)
self.fill_ele_f(self.tempXef1, self.tempYef1, self.tempErf1,
self.pho_ind_f1, self.pls_ind_f1,
self.hists_pipico['ex_ey_orth_fls1'],
self.hists_pipico['er_er_orth_fls1'],
self.hists_pipico['pho_pls_eng_orth_fls1'],
self.num_events_orth_f)
self.fill_ele_f(self.tempXef2, self.tempYef2, self.tempErf2,
self.pho_ind_f2, self.pls_ind_f2,
self.hists_pipico['ex_ey_orth_fls2'],
self.hists_pipico['er_er_orth_fls2'],
self.hists_pipico['pho_pls_eng_orth_fls2'],
self.num_events_orth_f)
if self.Ps > 0 or self.Pl > 0 or self.Pm > 0:
self.fill_ele(self.tempXe, self.tempYe, self.tempEr, pho_ind,
pls_ind, self.hists_pipico['ex_ey_pg'],
self.hists_pipico['er_er_pg'],
self.hists_pipico['pho_pls_eng_pg'], self.num_events)
self.fill_ele_f(self.tempXef1, self.tempYef1, self.tempErf1,
self.pho_ind_f1, self.pls_ind_f1,
self.hists_pipico['ex_ey_fls1'],
self.hists_pipico['er_er_pg_fls1'],
self.hists_pipico['pho_pls_eng_fls1'],
self.num_events_f)
self.fill_ele_f(self.tempXef2, self.tempYef2, self.tempErf2,
self.pho_ind_f2, self.pls_ind_f2,
self.hists_pipico['ex_ey_fls2'],
self.hists_pipico['er_er_pg_fls2'],
self.hists_pipico['pho_pls_eng_fls2'],
self.num_events_f)
def fill_ele(self, tempXe0, tempYe0, tempEr0, pho_ind0, pls_ind0, eXY0,
eReR0, pho_pls_eng0, num_events0):
for itemp in range(len(tempXe0)):
eXY0[tempXe0[itemp], tempYe0[itemp]] += 1
for itemp in range(len(tempEr0)):
eReR0[tempEr0[itemp], tempEr0] += 1
pho_pls_eng0[pho_ind0, pls_ind0] += 1
num_events0 += 1
def fill_ele_f(self, tempXe0, tempYe0, tempEr0, pho_ind0, pls_ind0, eXY0,
eReR0, pho_pls_eng0, num_events0):
if len(tempXe0) > 0:
self.fill_ele(tempXe0, tempYe0, tempEr0, pho_ind0, pls_ind0, eXY0,
eReR0, pho_pls_eng0, num_events0)
def reset_coin_var(self):
self.P1 = 0
self.P2 = 0
self.P12 = 0
self.tempX1ind = []
self.tempY1ind = []
self.tempX2ind = []
self.tempY2ind = []
self.tempX1 = []
self.tempY1 = []
self.tempT1 = []
self.tempT1_f = []
self.tempX2 = []
self.tempY2 = []
self.tempT2 = []
self.tempT2_f = []
self.tempXe = []
self.tempYe = []
self.tempXe_f1 = []
self.tempYe_f1 = []
self.tempXe_f2 = []
self.tempYe_f2 = []
self.tempEr = []
self.tempErf1 = []
self.tempErf2 = []
self.tempEng1_arr = []
self.tempPx1_arr = []
self.tempPy1_arr = []
self.tempPz1_arr = []
self.tempEng2_arr = []
self.tempPx2_arr = []
self.tempPy2_arr = []
self.tempPz2_arr = []
self.tempEng1ind_arr = []
self.tempPx1ind_arr = []
self.tempPy1ind_arr = []
self.tempPz1ind_arr = []
self.tempEng2ind_arr = []
self.tempPx2ind_arr = []
self.tempPy2ind_arr = []
self.tempPz2ind_arr = []
self.tempX1ind_arr = []
self.tempY1ind_arr = []
self.tempX2ind_arr = []
self.tempY2ind_arr = []
# self.num_eventss = 0
# self.num_eventsl = 0
self.Ps = 0
self.Pl = 0
self.Pm = 0
self.P_orth = 0
self.P_para = 0
def save_var(self, h5f):
grp = h5f.create_group('PiPiCoGate_' + self.gateName)
# grp.create_dataset('XY',data = self.XY)
# grp.create_dataset('XT',data = self.XT)
# grp.create_dataset('YT',data = self.YT)
grp.create_dataset(
'gateInfo_tof1s_tof1l_tof2s_tof2l_pipixbin_pipiybin',
data=np.array([
self.tof1s, self.tof1l, self.tof2s, self.tof2l,
self.pipico_xbin, self.pipico_ybin
]))
for histname in self.parent.hist_names_pipico:
try:
grp.create_dataset(histname,
data=self.hists_pipico_all[histname])
except Exception as e:
print(histname + ' failed to be saved.')
print(e)
def reduce(self):
for histname in self.parent.hist_names_pipico:
try:
self.time_a = time.time()
MPI.COMM_WORLD.Reduce(self.hists_pipico[histname],
self.hists_pipico_all[histname])
self.time_b = time.time()
print(histname + ' reduced by ' +
'Rank {0} in {1:.2f} seconds'.format(
self.parent.mpi_rank, self.time_b - self.time_a))
except Exception as e:
print(histname + ' failed to be redueced by ' +
str(self.parent.mpi_rank))
print(e)
def processDataE(self, irun, ievt):
if self.temp_irun != irun:
self.psana_source = psana.DataSource(self.parent.source)
for ii, rr in enumerate(self.psana_source.runs()):
if ii == irun:
self.temp_irun = irun
self.temprun = rr
break
self.temptimes = self.temprun.times()
self.temp_len_run = len(self.temptimes)
if ievt < 0 or ievt >= self.temp_len_run:
return [], [], [], None, None
event = {'evt': self.temprun.event(self.temptimes[ievt])}
if event['evt'] is None:
return [], [], [], None, None
self.extractE(event, self)
if self.eImage_f is None or self.pulse_eng_f is None or self.photon_eng_f is None:
return [], [], [], None, None
photon_eng_ind = next((pho_eng_ind for pho_eng_ind, pho_eng_ele in
enumerate(self.parent.vars_axis['phoeng'])
if pho_eng_ele > self.photon_eng_f), 0) - 1
pulse_eng_ind = next((pls_eng_ind for pls_eng_ind, pls_eng_ele in
enumerate(self.parent.vars_axis['plseng'])
if pls_eng_ele > self.pulse_eng_f), 0) - 1
if photon_eng_ind == -1 or pulse_eng_ind == -1:
return [], [], [], None, None
eXinds = []
eYinds = []
eRinds = []
e_peaks = self.alg.peak_finder_v4r2(self.eImage_f,
thr_low=self.parent.thr_low,
thr_high=self.parent.thr_high,
rank=self.parent.rank,
r0=self.parent.r0,
dr=self.parent.dr)
eXs = e_peaks[:, 1].astype(int)
eYs = e_peaks[:, 2].astype(int)
eRadius, eAngle = self.parent.cart2polar(eXs, eYs)
for e_ind in range(len(eXs)):
tempRind = next(
(eR_ind
for eR_ind, eR_ele in enumerate(self.parent.vars_axis['er'])
if eR_ele > eRadius[e_ind]), 0) - 1
# tempAind = next((eA_ind for eA_ind, eA_ele in enumerate(self.eAaxis) if eA_ele > eAngle[e_ind]),0)
tempXind = next(
(eX_ind
for eX_ind, eX_ele in enumerate(self.parent.vars_axis['ex'])
if eX_ele > eXs[e_ind]), 0) - 1
tempYind = next(
(eY_ind
for eY_ind, eY_ele in enumerate(self.parent.vars_axis['ey'])
if eY_ele > eYs[e_ind]), 0) - 1
# if tempRind != 0 and tempAind != 0 and tempXind != 0 and tempYind != 0:
if tempXind != -1 and tempYind != -1:
eXinds.append(tempXind)
eYinds.append(tempYind)
eRinds.append(tempRind)
if len(eXinds) > 0:
return eXinds, eYinds, eRinds, photon_eng_ind, pulse_eng_ind
else:
return [], [], [], None, None
import psana
ds = psana.DataSource('exp=xpptut15:run=54:smd')
det = psana.Detector('cspad')
from ImgAlgos.PyAlgos import PyAlgos
alg = PyAlgos()
for nevent, evt in enumerate(ds.events()):
if nevent >= 2: break
nda = det.calib(evt)
if nda is None: continue
thr = 20
numpix = alg.number_of_pix_above_thr(nda, thr)
totint = alg.intensity_of_pix_above_thr(nda, thr)
print '%d pixels have total intensity %5.1f above threshold %5.1f' % (
numpix, totint, thr)
class PeakFinder:
def __init__(self,exp,run,detname,evt,detector,algorithm,hitParam_alg_npix_min,hitParam_alg_npix_max,
hitParam_alg_amax_thr,hitParam_alg_atot_thr,hitParam_alg_son_min,
streakMask_on,streakMask_sigma,streakMask_width,userMask_path,psanaMask_on,psanaMask_calib,
psanaMask_status,psanaMask_edges,psanaMask_central,psanaMask_unbond,psanaMask_unbondnrs,
medianFilterOn=0, medianRank=5, radialFilterOn=0, distance=0.0, windows=None, **kwargs):
self.exp = exp
self.run = run
self.detname = detname
self.det = detector
self.algorithm = algorithm
self.maxRes = 0
self.npix_min=hitParam_alg_npix_min
self.npix_max=hitParam_alg_npix_max
self.amax_thr=hitParam_alg_amax_thr
self.atot_thr=hitParam_alg_atot_thr
self.son_min=hitParam_alg_son_min
self.streakMask_on = str2bool(streakMask_on)
self.streakMask_sigma = streakMask_sigma
self.streakMask_width = streakMask_width
self.userMask_path = userMask_path
self.psanaMask_on = str2bool(psanaMask_on)
self.psanaMask_calib = str2bool(psanaMask_calib)
self.psanaMask_status = str2bool(psanaMask_status)
self.psanaMask_edges = str2bool(psanaMask_edges)
self.psanaMask_central = str2bool(psanaMask_central)
self.psanaMask_unbond = str2bool(psanaMask_unbond)
self.psanaMask_unbondnrs = str2bool(psanaMask_unbondnrs)
self.medianFilterOn = medianFilterOn
self.medianRank = medianRank
self.radialFilterOn = radialFilterOn
self.distance = distance
self.windows = windows
self.userMask = None
self.psanaMask = None
self.streakMask = None
self.userPsanaMask = None
self.combinedMask = None
# Make user mask
if self.userMask_path is not None:
self.userMask = np.load(self.userMask_path)
# Make psana mask
if self.psanaMask_on:
self.psanaMask = detector.mask(evt, calib=self.psanaMask_calib, status=self.psanaMask_status,
edges=self.psanaMask_edges, central=self.psanaMask_central,
unbond=self.psanaMask_unbond, unbondnbrs=self.psanaMask_unbondnrs)
# Combine userMask and psanaMask
self.userPsanaMask = np.ones_like(self.det.calib(evt))
if self.userMask is not None:
self.userPsanaMask *= self.userMask
if self.psanaMask is not None:
self.userPsanaMask *= self.psanaMask
# Powder of hits and misses
self.powderHits = np.zeros_like(self.userPsanaMask)
self.powderMisses = np.zeros_like(self.userPsanaMask)
self.alg = PyAlgos(windows=self.windows, mask=self.userPsanaMask, pbits=0)
# set peak-selector parameters:
self.alg.set_peak_selection_pars(npix_min=self.npix_min, npix_max=self.npix_max, \
amax_thr=self.amax_thr, atot_thr=self.atot_thr, \
son_min=self.son_min)
# set algorithm specific parameters
if algorithm == 1:
self.hitParam_alg1_thr_low = kwargs["alg1_thr_low"]
self.hitParam_alg1_thr_high = kwargs["alg1_thr_high"]
self.hitParam_alg1_rank = int(kwargs["alg1_rank"])
self.hitParam_alg1_radius = int(kwargs["alg1_radius"])
self.hitParam_alg1_dr = kwargs["alg1_dr"]
elif algorithm == 3:
self.hitParam_alg3_rank = kwargs["alg3_rank"]
self.hitParam_alg3_r0 = int(kwargs["alg3_r0"])
self.hitParam_alg3_dr = kwargs["alg3_dr"]
elif algorithm == 4:
self.hitParam_alg4_thr_low = kwargs["alg4_thr_low"]
self.hitParam_alg4_thr_high = kwargs["alg4_thr_high"]
self.hitParam_alg4_rank = int(kwargs["alg4_rank"])
self.hitParam_alg4_r0 = int(kwargs["alg4_r0"])
self.hitParam_alg4_dr = kwargs["alg4_dr"]
self.maxNumPeaks = 2048
self.StreakMask = myskbeam.StreakMask(self.det, evt, width=self.streakMask_width, sigma=self.streakMask_sigma)
self.cx, self.cy = self.det.point_indexes(evt, pxy_um=(0, 0))
self.iX = np.array(self.det.indexes_x(evt), dtype=np.int64)
self.iY = np.array(self.det.indexes_y(evt), dtype=np.int64)
if len(self.iX.shape) == 2:
self.iX = np.expand_dims(self.iX, axis=0)
self.iY = np.expand_dims(self.iY, axis=0)
# Initialize radial background subtraction
self.setupExperiment()
if self.radialFilterOn:
self.setupRadialBackground()
self.updatePolarizationFactor()
def setupExperiment(self):
self.ds = psana.DataSource('exp=' + str(self.exp) + ':run=' + str(self.run) + ':idx')
self.run = self.ds.runs().next()
self.times = self.run.times()
self.eventTotal = len(self.times)
self.env = self.ds.env()
self.evt = self.run.event(self.times[0])
self.det = psana.Detector(str(self.detname), self.env)
self.det.do_reshape_2d_to_3d(flag=True)
def setupRadialBackground(self):
self.geo = self.det.geometry(self.run) # self.geo = GeometryAccess(self.parent.geom.calibPath+'/'+self.parent.geom.calibFile)
self.xarr, self.yarr, self.zarr = self.geo.get_pixel_coords()
self.ix = self.det.indexes_x(self.evt)
self.iy = self.det.indexes_y(self.evt)
if self.ix is None:
self.iy = np.tile(np.arange(self.userMask.shape[1]), [self.userMask.shape[2], 1])
self.ix = np.transpose(self.iy)
self.iX = np.array(self.ix, dtype=np.int64)
self.iY = np.array(self.iy, dtype=np.int64)
if len(self.iX.shape) == 2:
self.iX = np.expand_dims(self.iX, axis=0)
self.iY = np.expand_dims(self.iY, axis=0)
self.mask = self.geo.get_pixel_mask( mbits=0377) # mask for 2x1 edges, two central columns, and unbound pixels with their neighbours
self.rb = RadialBkgd(self.xarr, self.yarr, mask=self.mask, radedges=None, nradbins=100,
phiedges=(0, 360), nphibins=1)
def updatePolarizationFactor(self):
self.pf = polarization_factor(self.rb.pixel_rad(), self.rb.pixel_phi(), self.distance * 1e6) # convert to um
def findPeaks(self, calib, evt):
if self.streakMask_on: # make new streak mask
self.streakMask = self.StreakMask.getStreakMaskCalib(evt)
# Apply background correction
if self.medianFilterOn:
calib -= median_filter_ndarr(calib, self.medianRank)
if self.radialFilterOn:
self.pf.shape = calib.shape # FIXME: shape is 1d
calib = self.rb.subtract_bkgd(calib * self.pf)
calib.shape = self.userPsanaMask.shape # FIXME: shape is 1d
if self.streakMask is not None:
self.combinedMask = self.userPsanaMask * self.streakMask
else:
self.combinedMask = self.userPsanaMask
# set new mask
#self.alg = PyAlgos(windows=self.windows, mask=self.combinedMask, pbits=0)
# set peak-selector parameters:
#self.alg.set_peak_selection_pars(npix_min=self.npix_min, npix_max=self.npix_max, \
# amax_thr=self.amax_thr, atot_thr=self.atot_thr, \
# son_min=self.son_min)
self.alg.set_mask(self.combinedMask) # This doesn't work reliably
# set algorithm specific parameters
if self.algorithm == 1:
# v1 - aka Droplet Finder - two-threshold peak-finding algorithm in restricted region
# around pixel with maximal intensity.
self.peaks = self.alg.peak_finder_v4r2(calib,
thr_low=self.hitParam_alg1_thr_low,
thr_high=self.hitParam_alg1_thr_high,
rank=self.hitParam_alg1_rank,
r0=self.hitParam_alg1_radius,
dr=self.hitParam_alg1_dr)
elif self.algorithm == 3:
self.peaks = self.alg.peak_finder_v3(calib, rank=self.hitParam_alg3_rank, r0=self.hitParam_alg3_r0, dr=self.hitParam_alg3_dr)
elif self.algorithm == 4:
# v4 - aka iDroplet Finder - two-threshold peak-finding algorithm in restricted region
# around pixel with maximal intensity.
self.peaks = self.alg.peak_finder_v4(calib, thr_low=self.hitParam_alg4_thr_low, thr_high=self.hitParam_alg4_thr_high, \
rank=self.hitParam_alg4_rank, r0=self.hitParam_alg4_r0, dr=self.hitParam_alg4_dr)
self.numPeaksFound = self.peaks.shape[0]
if self.numPeaksFound > 0:
cenX = self.iX[np.array(self.peaks[:, 0], dtype=np.int64), np.array(self.peaks[:, 1], dtype=np.int64), np.array(
self.peaks[:, 2], dtype=np.int64)] + 0.5
cenY = self.iY[np.array(self.peaks[:, 0], dtype=np.int64), np.array(self.peaks[:, 1], dtype=np.int64), np.array(
self.peaks[:, 2], dtype=np.int64)] + 0.5
self.maxRes = getMaxRes(cenX, cenY, self.cx, self.cy)
else:
self.maxRes = 0
if self.numPeaksFound >= 15:
self.powderHits = np.maximum(self.powderHits, calib)
else:
self.powderMisses = np.maximum(self.powderMisses, calib)
class PeakFinder:
def __init__(self,
exp,
run,
detname,
evt,
detector,
algorithm,
hitParam_alg_npix_min,
hitParam_alg_npix_max,
hitParam_alg_amax_thr,
hitParam_alg_atot_thr,
hitParam_alg_son_min,
streakMask_on,
streakMask_sigma,
streakMask_width,
userMask_path,
psanaMask_on,
psanaMask_calib,
psanaMask_status,
psanaMask_edges,
psanaMask_central,
psanaMask_unbond,
psanaMask_unbondnrs,
medianFilterOn=0,
medianRank=5,
radialFilterOn=0,
distance=0.0,
windows=None,
**kwargs):
self.exp = exp
self.run = run
self.detname = detname
self.det = detector
self.algorithm = algorithm
self.maxRes = 0
self.npix_min = hitParam_alg_npix_min
self.npix_max = hitParam_alg_npix_max
self.amax_thr = hitParam_alg_amax_thr
self.atot_thr = hitParam_alg_atot_thr
self.son_min = hitParam_alg_son_min
self.streakMask_on = str2bool(streakMask_on)
self.streakMask_sigma = streakMask_sigma
self.streakMask_width = streakMask_width
self.userMask_path = userMask_path
self.psanaMask_on = str2bool(psanaMask_on)
self.psanaMask_calib = str2bool(psanaMask_calib)
self.psanaMask_status = str2bool(psanaMask_status)
self.psanaMask_edges = str2bool(psanaMask_edges)
self.psanaMask_central = str2bool(psanaMask_central)
self.psanaMask_unbond = str2bool(psanaMask_unbond)
self.psanaMask_unbondnrs = str2bool(psanaMask_unbondnrs)
self.medianFilterOn = medianFilterOn
self.medianRank = medianRank
self.radialFilterOn = radialFilterOn
self.distance = distance
self.windows = windows
self.userMask = None
self.psanaMask = None
self.streakMask = None
self.userPsanaMask = None
self.combinedMask = None
# Make user mask
if self.userMask_path is not None:
self.userMask = np.load(self.userMask_path)
# Make psana mask
if self.psanaMask_on:
self.psanaMask = detector.mask(evt,
calib=self.psanaMask_calib,
status=self.psanaMask_status,
edges=self.psanaMask_edges,
central=self.psanaMask_central,
unbond=self.psanaMask_unbond,
unbondnbrs=self.psanaMask_unbondnrs)
# Combine userMask and psanaMask
self.userPsanaMask = np.ones_like(self.det.calib(evt))
if self.userMask is not None:
self.userPsanaMask *= self.userMask
if self.psanaMask is not None:
self.userPsanaMask *= self.psanaMask
# Powder of hits and misses
self.powderHits = np.zeros_like(self.userPsanaMask)
self.powderMisses = np.zeros_like(self.userPsanaMask)
self.alg = PyAlgos(windows=self.windows,
mask=self.userPsanaMask,
pbits=0)
# set peak-selector parameters:
self.alg.set_peak_selection_pars(npix_min=self.npix_min, npix_max=self.npix_max, \
amax_thr=self.amax_thr, atot_thr=self.atot_thr, \
son_min=self.son_min)
# set algorithm specific parameters
if algorithm == 1:
self.hitParam_alg1_thr_low = kwargs["alg1_thr_low"]
self.hitParam_alg1_thr_high = kwargs["alg1_thr_high"]
self.hitParam_alg1_rank = int(kwargs["alg1_rank"])
self.hitParam_alg1_radius = int(kwargs["alg1_radius"])
self.hitParam_alg1_dr = kwargs["alg1_dr"]
elif algorithm == 3:
self.hitParam_alg3_rank = kwargs["alg3_rank"]
self.hitParam_alg3_r0 = int(kwargs["alg3_r0"])
self.hitParam_alg3_dr = kwargs["alg3_dr"]
elif algorithm == 4:
self.hitParam_alg4_thr_low = kwargs["alg4_thr_low"]
self.hitParam_alg4_thr_high = kwargs["alg4_thr_high"]
self.hitParam_alg4_rank = int(kwargs["alg4_rank"])
self.hitParam_alg4_r0 = int(kwargs["alg4_r0"])
self.hitParam_alg4_dr = kwargs["alg4_dr"]
self.maxNumPeaks = 2048
self.StreakMask = myskbeam.StreakMask(self.det,
evt,
width=self.streakMask_width,
sigma=self.streakMask_sigma)
self.cx, self.cy = self.det.point_indexes(evt, pxy_um=(0, 0))
self.iX = np.array(self.det.indexes_x(evt), dtype=np.int64)
self.iY = np.array(self.det.indexes_y(evt), dtype=np.int64)
if len(self.iX.shape) == 2:
self.iX = np.expand_dims(self.iX, axis=0)
self.iY = np.expand_dims(self.iY, axis=0)
# Initialize radial background subtraction
self.setupExperiment()
if self.radialFilterOn:
self.setupRadialBackground()
self.updatePolarizationFactor()
def setupExperiment(self):
self.ds = psana.DataSource('exp=' + str(self.exp) + ':run=' +
str(self.run) + ':idx')
self.run = self.ds.runs().next()
self.times = self.run.times()
self.eventTotal = len(self.times)
self.env = self.ds.env()
self.evt = self.run.event(self.times[0])
self.det = psana.Detector(str(self.detname), self.env)
self.det.do_reshape_2d_to_3d(flag=True)
def setupRadialBackground(self):
self.geo = self.det.geometry(
self.run
) # self.geo = GeometryAccess(self.parent.geom.calibPath+'/'+self.parent.geom.calibFile)
self.xarr, self.yarr, self.zarr = self.geo.get_pixel_coords()
self.ix = self.det.indexes_x(self.evt)
self.iy = self.det.indexes_y(self.evt)
if self.ix is None:
self.iy = np.tile(np.arange(self.userMask.shape[1]),
[self.userMask.shape[2], 1])
self.ix = np.transpose(self.iy)
self.iX = np.array(self.ix, dtype=np.int64)
self.iY = np.array(self.iy, dtype=np.int64)
if len(self.iX.shape) == 2:
self.iX = np.expand_dims(self.iX, axis=0)
self.iY = np.expand_dims(self.iY, axis=0)
self.mask = self.geo.get_pixel_mask(
mbits=0377
) # mask for 2x1 edges, two central columns, and unbound pixels with their neighbours
self.rb = RadialBkgd(self.xarr,
self.yarr,
mask=self.mask,
radedges=None,
nradbins=100,
phiedges=(0, 360),
nphibins=1)
def updatePolarizationFactor(self):
self.pf = polarization_factor(self.rb.pixel_rad(), self.rb.pixel_phi(),
self.distance * 1e6) # convert to um
def findPeaks(self, calib, evt):
if self.streakMask_on: # make new streak mask
self.streakMask = self.StreakMask.getStreakMaskCalib(evt)
# Apply background correction
if self.medianFilterOn:
calib -= median_filter_ndarr(calib, self.medianRank)
if self.radialFilterOn:
self.pf.shape = calib.shape # FIXME: shape is 1d
calib = self.rb.subtract_bkgd(calib * self.pf)
calib.shape = self.userPsanaMask.shape # FIXME: shape is 1d
if self.streakMask is not None:
self.combinedMask = self.userPsanaMask * self.streakMask
else:
self.combinedMask = self.userPsanaMask
# set new mask
#self.alg = PyAlgos(windows=self.windows, mask=self.combinedMask, pbits=0)
# set peak-selector parameters:
#self.alg.set_peak_selection_pars(npix_min=self.npix_min, npix_max=self.npix_max, \
# amax_thr=self.amax_thr, atot_thr=self.atot_thr, \
# son_min=self.son_min)
self.alg.set_mask(self.combinedMask) # This doesn't work reliably
# set algorithm specific parameters
if self.algorithm == 1:
# v1 - aka Droplet Finder - two-threshold peak-finding algorithm in restricted region
# around pixel with maximal intensity.
self.peaks = self.alg.peak_finder_v4r2(
calib,
thr_low=self.hitParam_alg1_thr_low,
thr_high=self.hitParam_alg1_thr_high,
rank=self.hitParam_alg1_rank,
r0=self.hitParam_alg1_radius,
dr=self.hitParam_alg1_dr)
elif self.algorithm == 3:
self.peaks = self.alg.peak_finder_v3(calib,
rank=self.hitParam_alg3_rank,
r0=self.hitParam_alg3_r0,
dr=self.hitParam_alg3_dr)
elif self.algorithm == 4:
# v4 - aka iDroplet Finder - two-threshold peak-finding algorithm in restricted region
# around pixel with maximal intensity.
self.peaks = self.alg.peak_finder_v4(calib, thr_low=self.hitParam_alg4_thr_low, thr_high=self.hitParam_alg4_thr_high, \
rank=self.hitParam_alg4_rank, r0=self.hitParam_alg4_r0, dr=self.hitParam_alg4_dr)
self.numPeaksFound = self.peaks.shape[0]
if self.numPeaksFound > 0:
cenX = self.iX[np.array(self.peaks[:, 0], dtype=np.int64),
np.array(self.peaks[:, 1], dtype=np.int64),
np.array(self.peaks[:, 2], dtype=np.int64)] + 0.5
cenY = self.iY[np.array(self.peaks[:, 0], dtype=np.int64),
np.array(self.peaks[:, 1], dtype=np.int64),
np.array(self.peaks[:, 2], dtype=np.int64)] + 0.5
self.maxRes = getMaxRes(cenX, cenY, self.cx, self.cy)
else:
self.maxRes = 0
if self.numPeaksFound >= 15:
self.powderHits = np.maximum(self.powderHits, calib)
else:
self.powderMisses = np.maximum(self.powderMisses, calib)
def updateClassification(self):
if self.parent.calib is not None:
if self.parent.mk.streakMaskOn:
self.parent.mk.initMask()
self.parent.mk.streakMask = self.parent.mk.StreakMask.getStreakMaskCalib(self.parent.evt)
if self.parent.mk.streakMask is None:
self.parent.mk.streakMaskAssem = None
else:
self.parent.mk.streakMaskAssem = self.parent.det.image(self.parent.evt, self.parent.mk.streakMask)
self.algInitDone = False
self.parent.mk.displayMask()
# update combined mask
self.parent.mk.combinedMask = np.ones_like(self.parent.calib)
if self.parent.mk.streakMask is not None and self.parent.mk.streakMaskOn is True:
self.parent.mk.combinedMask *= self.parent.mk.streakMask
if self.parent.mk.userMask is not None and self.parent.mk.userMaskOn is True:
self.parent.mk.combinedMask *= self.parent.mk.userMask
if self.parent.mk.psanaMask is not None and self.parent.mk.psanaMaskOn is True:
self.parent.mk.combinedMask *= self.parent.mk.psanaMask
# Peak output (0-16):
# 0 seg
# 1 row
# 2 col
# 3 npix: no. of pixels in the ROI intensities above threshold
# 4 amp_max: max intensity
# 5 amp_tot: sum of intensities
# 6,7: row_cgrav: center of mass
# 8,9: row_sigma
# 10,11,12,13: minimum bounding box
# 14: background
# 15: noise
# 16: signal over noise
if self.algorithm == 0: # No peak algorithm
self.peaks = None
self.drawPeaks()
else:
# Only initialize the hit finder algorithm once
if self.algInitDone is False:
self.windows = None
self.alg = []
self.alg = PyAlgos(windows=self.windows, mask=self.parent.mk.combinedMask, pbits=0)
# set peak-selector parameters:
if self.algorithm == 1:
self.alg.set_peak_selection_pars(npix_min=self.hitParam_alg1_npix_min, npix_max=self.hitParam_alg1_npix_max, \
amax_thr=self.hitParam_alg1_amax_thr, atot_thr=self.hitParam_alg1_atot_thr, \
son_min=self.hitParam_alg1_son_min)
elif self.algorithm == 2:
self.alg.set_peak_selection_pars(npix_min=self.hitParam_alg2_npix_min, npix_max=self.hitParam_alg2_npix_max, \
amax_thr=self.hitParam_alg2_amax_thr, atot_thr=self.hitParam_alg2_atot_thr, \
son_min=self.hitParam_alg2_son_min)
elif self.algorithm == 3:
self.alg.set_peak_selection_pars(npix_min=self.hitParam_alg3_npix_min, npix_max=self.hitParam_alg3_npix_max, \
amax_thr=self.hitParam_alg3_amax_thr, atot_thr=self.hitParam_alg3_atot_thr, \
son_min=self.hitParam_alg3_son_min)
elif self.algorithm == 4:
self.alg.set_peak_selection_pars(npix_min=self.hitParam_alg4_npix_min, npix_max=self.hitParam_alg4_npix_max, \
amax_thr=self.hitParam_alg4_amax_thr, atot_thr=self.hitParam_alg4_atot_thr, \
son_min=self.hitParam_alg4_son_min)
self.algInitDone = True
self.parent.calib = self.parent.calib * 1.0 # Neccessary when int is returned
if self.algorithm == 1:
# v1 - aka Droplet Finder - two-threshold peak-finding algorithm in restricted region
# around pixel with maximal intensity.
self.peakRadius = int(self.hitParam_alg1_radius)
self.peaks = self.alg.peak_finder_v4r2(self.parent.calib,
thr_low=self.hitParam_alg1_thr_low,
thr_high=self.hitParam_alg1_thr_high,
rank=int(self.hitParam_alg1_rank),
r0=self.peakRadius,
dr=self.hitParam_alg1_dr)
elif self.algorithm == 2:
# v2 - define peaks for regions of connected pixels above threshold
self.peakRadius = int(self.hitParam_alg2_r0)
self.peaks = self.alg.peak_finder_v2(self.parent.calib, thr=self.hitParam_alg2_thr, r0=self.peakRadius, dr=self.hitParam_alg2_dr)
elif self.algorithm == 3:
self.peakRadius = int(self.hitParam_alg3_r0)
self.peaks = self.alg.peak_finder_v3(self.parent.calib, rank=self.hitParam_alg3_rank, r0=self.peakRadius, dr=self.hitParam_alg3_dr)
elif self.algorithm == 4:
# v4 - aka Droplet Finder - the same as v1, but uses rank and r0 parameters in stead of common radius.
self.peakRadius = int(self.hitParam_alg4_r0)
self.peaks = self.alg.peak_finder_v4(self.parent.calib, thr_low=self.hitParam_alg4_thr_low, thr_high=self.hitParam_alg4_thr_high,
rank=self.hitParam_alg4_rank, r0=self.peakRadius, dr=self.hitParam_alg4_dr)
self.numPeaksFound = self.peaks.shape[0]
if self.parent.args.v >= 1: print "Num peaks found: ", self.numPeaksFound, self.peaks.shape
# update clen
self.parent.geom.updateClen('lcls')
self.parent.index.clearIndexedPeaks()
# Save image and peaks in cheetah cxi file
self.saveCheetahFormat('lcls')
if self.parent.index.showIndexedPeaks: self.parent.index.updateIndex()
self.drawPeaks()
if self.parent.args.v >= 1: print "Done updateClassification"
def __init__(self, source, monitor_params):
super(Onda, self).__init__(map_func=self.process_data,
reduce_func=self.collect_data,
source=source, monitor_params=monitor_params)
import psana
self.npix_min, self.npix_max= monitor_params['Opal']['npix_min'],monitor_params['Opal']['npix_max']
self.amax_thr, self.atot_thr, self.son_min = monitor_params['Opal']['amax_thr'], monitor_params['Opal']['atot_thr'], monitor_params['Opal']['son_min']
self.thr_low, self.thr_high = monitor_params['Opal']['thr_low'], monitor_params['Opal']['thr_high']
self.rank, self.r0, self.dr = monitor_params['Opal']['rank'], monitor_params['Opal']['r0'], monitor_params['Opal']['dr']
self.e_center_x, self.e_center_y = monitor_params['Opal']['e_center_x'],monitor_params['Opal']['e_center_y']
print 'coin_point1'
self.params_gen = monitor_params['General']
self.params_peakfinder_t, self.params_peakfinder_x, self.params_peakfinder_y = monitor_params['PeakFinderMcp'], monitor_params['PeakFinderX'], monitor_params['PeakFinderY']
self.params_hitfinder = monitor_params['HitFinder']
self.output_params = monitor_params['OutputLayer']
self.offset_acq = [0.01310443,0.02252858,0.02097541,0.01934959,-0.0001188]
self.t_channel = monitor_params['DetectorLayer']['mcp_channel']
self.x1_channel = monitor_params['DetectorLayer']['x1_channel']
self.x2_channel = monitor_params['DetectorLayer']['x2_channel']
self.y1_channel = monitor_params['DetectorLayer']['y1_channel']
self.y2_channel = monitor_params['DetectorLayer']['y2_channel']
#################################################################
self.Xmin, self.Ymin, self.Tmin = monitor_params['OutputLayer']['xmin'],monitor_params['OutputLayer']['ymin'],monitor_params['OutputLayer']['tmin']
self.Xmax, self.Ymax, self.Tmax = monitor_params['OutputLayer']['xmax'],monitor_params['OutputLayer']['ymax'],monitor_params['OutputLayer']['tmax']
self.Xbin, self.Ybin, self.Tbin = monitor_params['OutputLayer']['xbin'],monitor_params['OutputLayer']['ybin'],monitor_params['OutputLayer']['tbin']
self.Xbinnum = int((self.Xmax - self.Xmin)/self.Xbin)
self.Ybinnum = int((self.Ymax - self.Ymin)/self.Ybin)
self.Tbinnum = int((self.Tmax - self.Tmin)/self.Tbin)
self.Xaxis = np.linspace(self.Xmin, self.Xmax, self.Xbinnum)
self.Yaxis = np.linspace(self.Ymin, self.Ymax, self.Ybinnum)
self.Taxis = np.linspace(self.Tmin, self.Tmax, self.Tbinnum)
self.Xaxis_r = self.Xaxis[::-1]
self.Yaxis_r = self.Yaxis[::-1]
self.Taxis_r = self.Taxis[::-1]
self.XaxisM = (self.Xaxis[:-1] + self.Xaxis[1:])/2
self.YaxisM = (self.Yaxis[:-1] + self.Yaxis[1:])/2
self.TaxisM = (self.Taxis[:-1] + self.Taxis[1:])/2
self.eXmin, self.eYmin, self.eRmin, self.eAmin = monitor_params['OutputLayer']['exmin'],monitor_params['OutputLayer']['eymin'],monitor_params['OutputLayer']['ermin'],monitor_params['OutputLayer']['eamin']
self.eXmax, self.eYmax, self.eRmax, self.eAmax = monitor_params['OutputLayer']['exmax'],monitor_params['OutputLayer']['eymax'],monitor_params['OutputLayer']['ermax'],monitor_params['OutputLayer']['eamax']
self.eXbin, self.eYbin, self.eRbin, self.eAbin = monitor_params['OutputLayer']['exbin'],monitor_params['OutputLayer']['eybin'],monitor_params['OutputLayer']['erbin'],monitor_params['OutputLayer']['eabin']
self.photon_eng_bin, self.pulse_eng_bin = monitor_params['OutputLayer']['photon_eng_bin'],monitor_params['OutputLayer']['pulse_eng_bin']
self.photon_eng_min, self.pulse_eng_min = monitor_params['OutputLayer']['photon_eng_min'],monitor_params['OutputLayer']['pulse_eng_min']
self.photon_eng_max, self.pulse_eng_max = monitor_params['OutputLayer']['photon_eng_max'],monitor_params['OutputLayer']['pulse_eng_max']
self.photon_eng_binnum = int((self.photon_eng_max - self.photon_eng_min)/self.photon_eng_bin)
self.pulse_eng_binnum = int((self.pulse_eng_max - self.pulse_eng_min)/self.pulse_eng_bin)
self.photon_eng_axis = np.linspace(self.photon_eng_min, self.photon_eng_max, self.photon_eng_binnum)
self.pulse_eng_axis = np.linspace(self.pulse_eng_min, self.pulse_eng_max, self.pulse_eng_binnum)
self.eXbinnum = int((self.eXmax - self.eXmin)/self.eXbin)
self.eYbinnum = int((self.eYmax - self.eYmin)/self.eYbin)
self.eAbinnum = int((self.eAmax - self.eAmin)/self.eAbin)
self.eRbinnum = int((self.eRmax - self.eRmin)/self.eRbin)
print 'binnum:', self.eXbinnum, self.eYbinnum
self.eXaxis = np.linspace(self.eXmin, self.eXmax, self.eXbinnum)
self.eYaxis = np.linspace(self.eYmin, self.eYmax, self.eYbinnum)
self.eAaxis = np.linspace(self.eAmin, self.eAmax, self.eAbinnum)
self.eRaxis = np.linspace(self.eRmin, self.eRmax, self.eRbinnum)
self.eXaxis_r = self.eXaxis[::-1]
self.eYaxis_r = self.eYaxis[::-1]
self.eAaxis_r = self.eAaxis[::-1]
self.eRaxis_r = self.eRaxis[::-1]
self.eXaxisM = (self.eXaxis[:-1] + self.eXaxis[1:])/2
self.eYaxisM = (self.eYaxis[:-1] + self.eYaxis[1:])/2
self.eAaxisM = (self.eAaxis[:-1] + self.eAaxis[1:])/2
self.eRaxisM = (self.eRaxis[:-1] + self.eRaxis[1:])/2
self.pho_pls_eng = np.zeros([self.photon_eng_binnum-1 , self.pulse_eng_binnum-1])
self.eXY = np.zeros([self.photon_eng_binnum-1 , self.pulse_eng_binnum-1, self.eXbinnum-1, self.eYbinnum-1])
self.eAR = np.zeros([self.photon_eng_binnum-1 , self.pulse_eng_binnum-1, self.eAbinnum-1, self.eRbinnum-1])
self.eA = np.zeros([self.photon_eng_binnum-1 , self.pulse_eng_binnum-1, self.eAbinnum-1])
self.eR = np.zeros([self.photon_eng_binnum-1 , self.pulse_eng_binnum-1, self.eRbinnum-1])
self.PeakFinderT = AcqirisPeakFinder(self.params_peakfinder_t)
self.PeakFinderX = AcqirisPeakFinder(self.params_peakfinder_x)
self.PeakFinderY = AcqirisPeakFinder(self.params_peakfinder_y)
self.hit_list = []
self.HitFinder = BasicHitFinder(self.params_hitfinder)
self.num_events_all = 0
self.mask = np.ones([1024,1024])
self.mask[350:650, 350:650] = 0
self.mask[:, :350] = 0
self.alg = PyAlgos()
self.alg.set_peak_selection_pars(npix_min=self.npix_min, npix_max=self.npix_max, amax_thr=self.amax_thr, atot_thr=self.atot_thr, son_min=self.son_min)
if self.role == 'master':
self.collected_data = {}
self.publish_ip = self.params_gen['publish_ip']
self.publish_port = self.params_gen['publish_port']
self.speed_rep_int = self.params_gen['speed_report_interval']
self.accumulated_shots = self.params_gen['accumulated_shots']
self.peaks_to_send_string = self.params_gen['peaks_to_send'].split(',')
self.peaks_to_send = (int(self.peaks_to_send_string[0]),
int(self.peaks_to_send_string[1]))
print('Starting the monitor...')
sys.stdout.flush()
zmq_mon.init_zmq_to_gui(self, self.publish_ip, self.publish_port)
self.num_events = 0
self.old_time = time.time()
self.time = None
self.data_accumulator = []
if self.role == 'worker':
self.XYw = np.zeros([self.Xbinnum-1, self.Ybinnum-1])
self.eXYw = np.zeros([self.eXbinnum-1, self.eYbinnum-1])
self.eARw = np.zeros([self.eAbinnum-1, self.eRbinnum-1])
self.eRw = np.zeros([1, self.eRbinnum-1])
self.eAw = np.zeros([1, self.eAbinnum-1])
self.Tofw = np.zeros([1, self.Tbinnum-1])
self.XTw = np.zeros([self.Tbinnum-1, self.Xbinnum-1])
self.YTw = np.zeros([self.Tbinnum-1, self.Ybinnum-1])
self.TSumXw = np.zeros([self.Tbinnum-1,1])
self.TSumYw = np.zeros([self.Tbinnum-1,1])
self.PiPiCow = np.zeros([self.Tbinnum-2,self.Tbinnum-2])
self.results_dict = {}
print('Starting worker: {0}.'.format(self.mpi_rank))
sys.stdout.flush()
return
class Onda(MasterWorker):
def __init__(self, source, monitor_params):
super(Onda, self).__init__(map_func=self.process_data,
reduce_func=self.collect_data,
source=source, monitor_params=monitor_params)
import psana
self.npix_min, self.npix_max= monitor_params['Opal']['npix_min'],monitor_params['Opal']['npix_max']
self.amax_thr, self.atot_thr, self.son_min = monitor_params['Opal']['amax_thr'], monitor_params['Opal']['atot_thr'], monitor_params['Opal']['son_min']
self.thr_low, self.thr_high = monitor_params['Opal']['thr_low'], monitor_params['Opal']['thr_high']
self.rank, self.r0, self.dr = monitor_params['Opal']['rank'], monitor_params['Opal']['r0'], monitor_params['Opal']['dr']
self.e_center_x, self.e_center_y = monitor_params['Opal']['e_center_x'],monitor_params['Opal']['e_center_y']
print 'coin_point1'
self.params_gen = monitor_params['General']
self.params_peakfinder_t, self.params_peakfinder_x, self.params_peakfinder_y = monitor_params['PeakFinderMcp'], monitor_params['PeakFinderX'], monitor_params['PeakFinderY']
self.params_hitfinder = monitor_params['HitFinder']
self.output_params = monitor_params['OutputLayer']
self.offset_acq = [0.01310443,0.02252858,0.02097541,0.01934959,-0.0001188]
self.t_channel = monitor_params['DetectorLayer']['mcp_channel']
self.x1_channel = monitor_params['DetectorLayer']['x1_channel']
self.x2_channel = monitor_params['DetectorLayer']['x2_channel']
self.y1_channel = monitor_params['DetectorLayer']['y1_channel']
self.y2_channel = monitor_params['DetectorLayer']['y2_channel']
#################################################################
self.Xmin, self.Ymin, self.Tmin = monitor_params['OutputLayer']['xmin'],monitor_params['OutputLayer']['ymin'],monitor_params['OutputLayer']['tmin']
self.Xmax, self.Ymax, self.Tmax = monitor_params['OutputLayer']['xmax'],monitor_params['OutputLayer']['ymax'],monitor_params['OutputLayer']['tmax']
self.Xbin, self.Ybin, self.Tbin = monitor_params['OutputLayer']['xbin'],monitor_params['OutputLayer']['ybin'],monitor_params['OutputLayer']['tbin']
self.Xbinnum = int((self.Xmax - self.Xmin)/self.Xbin)
self.Ybinnum = int((self.Ymax - self.Ymin)/self.Ybin)
self.Tbinnum = int((self.Tmax - self.Tmin)/self.Tbin)
self.Xaxis = np.linspace(self.Xmin, self.Xmax, self.Xbinnum)
self.Yaxis = np.linspace(self.Ymin, self.Ymax, self.Ybinnum)
self.Taxis = np.linspace(self.Tmin, self.Tmax, self.Tbinnum)
self.Xaxis_r = self.Xaxis[::-1]
self.Yaxis_r = self.Yaxis[::-1]
self.Taxis_r = self.Taxis[::-1]
self.XaxisM = (self.Xaxis[:-1] + self.Xaxis[1:])/2
self.YaxisM = (self.Yaxis[:-1] + self.Yaxis[1:])/2
self.TaxisM = (self.Taxis[:-1] + self.Taxis[1:])/2
self.eXmin, self.eYmin, self.eRmin, self.eAmin = monitor_params['OutputLayer']['exmin'],monitor_params['OutputLayer']['eymin'],monitor_params['OutputLayer']['ermin'],monitor_params['OutputLayer']['eamin']
self.eXmax, self.eYmax, self.eRmax, self.eAmax = monitor_params['OutputLayer']['exmax'],monitor_params['OutputLayer']['eymax'],monitor_params['OutputLayer']['ermax'],monitor_params['OutputLayer']['eamax']
self.eXbin, self.eYbin, self.eRbin, self.eAbin = monitor_params['OutputLayer']['exbin'],monitor_params['OutputLayer']['eybin'],monitor_params['OutputLayer']['erbin'],monitor_params['OutputLayer']['eabin']
self.photon_eng_bin, self.pulse_eng_bin = monitor_params['OutputLayer']['photon_eng_bin'],monitor_params['OutputLayer']['pulse_eng_bin']
self.photon_eng_min, self.pulse_eng_min = monitor_params['OutputLayer']['photon_eng_min'],monitor_params['OutputLayer']['pulse_eng_min']
self.photon_eng_max, self.pulse_eng_max = monitor_params['OutputLayer']['photon_eng_max'],monitor_params['OutputLayer']['pulse_eng_max']
self.photon_eng_binnum = int((self.photon_eng_max - self.photon_eng_min)/self.photon_eng_bin)
self.pulse_eng_binnum = int((self.pulse_eng_max - self.pulse_eng_min)/self.pulse_eng_bin)
self.photon_eng_axis = np.linspace(self.photon_eng_min, self.photon_eng_max, self.photon_eng_binnum)
self.pulse_eng_axis = np.linspace(self.pulse_eng_min, self.pulse_eng_max, self.pulse_eng_binnum)
self.eXbinnum = int((self.eXmax - self.eXmin)/self.eXbin)
self.eYbinnum = int((self.eYmax - self.eYmin)/self.eYbin)
self.eAbinnum = int((self.eAmax - self.eAmin)/self.eAbin)
self.eRbinnum = int((self.eRmax - self.eRmin)/self.eRbin)
print 'binnum:', self.eXbinnum, self.eYbinnum
self.eXaxis = np.linspace(self.eXmin, self.eXmax, self.eXbinnum)
self.eYaxis = np.linspace(self.eYmin, self.eYmax, self.eYbinnum)
self.eAaxis = np.linspace(self.eAmin, self.eAmax, self.eAbinnum)
self.eRaxis = np.linspace(self.eRmin, self.eRmax, self.eRbinnum)
self.eXaxis_r = self.eXaxis[::-1]
self.eYaxis_r = self.eYaxis[::-1]
self.eAaxis_r = self.eAaxis[::-1]
self.eRaxis_r = self.eRaxis[::-1]
self.eXaxisM = (self.eXaxis[:-1] + self.eXaxis[1:])/2
self.eYaxisM = (self.eYaxis[:-1] + self.eYaxis[1:])/2
self.eAaxisM = (self.eAaxis[:-1] + self.eAaxis[1:])/2
self.eRaxisM = (self.eRaxis[:-1] + self.eRaxis[1:])/2
self.pho_pls_eng = np.zeros([self.photon_eng_binnum-1 , self.pulse_eng_binnum-1])
self.eXY = np.zeros([self.photon_eng_binnum-1 , self.pulse_eng_binnum-1, self.eXbinnum-1, self.eYbinnum-1])
self.eAR = np.zeros([self.photon_eng_binnum-1 , self.pulse_eng_binnum-1, self.eAbinnum-1, self.eRbinnum-1])
self.eA = np.zeros([self.photon_eng_binnum-1 , self.pulse_eng_binnum-1, self.eAbinnum-1])
self.eR = np.zeros([self.photon_eng_binnum-1 , self.pulse_eng_binnum-1, self.eRbinnum-1])
self.PeakFinderT = AcqirisPeakFinder(self.params_peakfinder_t)
self.PeakFinderX = AcqirisPeakFinder(self.params_peakfinder_x)
self.PeakFinderY = AcqirisPeakFinder(self.params_peakfinder_y)
self.hit_list = []
self.HitFinder = BasicHitFinder(self.params_hitfinder)
self.num_events_all = 0
self.mask = np.ones([1024,1024])
self.mask[350:650, 350:650] = 0
self.mask[:, :350] = 0
self.alg = PyAlgos()
self.alg.set_peak_selection_pars(npix_min=self.npix_min, npix_max=self.npix_max, amax_thr=self.amax_thr, atot_thr=self.atot_thr, son_min=self.son_min)
if self.role == 'master':
self.collected_data = {}
self.publish_ip = self.params_gen['publish_ip']
self.publish_port = self.params_gen['publish_port']
self.speed_rep_int = self.params_gen['speed_report_interval']
self.accumulated_shots = self.params_gen['accumulated_shots']
self.peaks_to_send_string = self.params_gen['peaks_to_send'].split(',')
self.peaks_to_send = (int(self.peaks_to_send_string[0]),
int(self.peaks_to_send_string[1]))
print('Starting the monitor...')
sys.stdout.flush()
zmq_mon.init_zmq_to_gui(self, self.publish_ip, self.publish_port)
self.num_events = 0
self.old_time = time.time()
self.time = None
self.data_accumulator = []
if self.role == 'worker':
self.XYw = np.zeros([self.Xbinnum-1, self.Ybinnum-1])
self.eXYw = np.zeros([self.eXbinnum-1, self.eYbinnum-1])
self.eARw = np.zeros([self.eAbinnum-1, self.eRbinnum-1])
self.eRw = np.zeros([1, self.eRbinnum-1])
self.eAw = np.zeros([1, self.eAbinnum-1])
self.Tofw = np.zeros([1, self.Tbinnum-1])
self.XTw = np.zeros([self.Tbinnum-1, self.Xbinnum-1])
self.YTw = np.zeros([self.Tbinnum-1, self.Ybinnum-1])
self.TSumXw = np.zeros([self.Tbinnum-1,1])
self.TSumYw = np.zeros([self.Tbinnum-1,1])
self.PiPiCow = np.zeros([self.Tbinnum-2,self.Tbinnum-2])
self.results_dict = {}
print('Starting worker: {0}.'.format(self.mpi_rank))
sys.stdout.flush()
return
def cart2polar_img(self, x, y, intensity):
x1 = x-self.e_center_x
y1 = y-self.e_center_y
r = np.sqrt(x1**2 + y1**2)
angle = np.arctan2(y1, x1)
return r, angle, intensity*r
def cart2polar(self, x, y):
x1 = x-self.e_center_x
y1 = y-self.e_center_y
r = np.sqrt(x1**2 + y1**2)
angle = np.arctan2(y1, x1)
return r, angle
def process_data(self):
self.results_dict = {}
MCPinds = []
Tofinds = []
Xinds = []
Yinds =[]
eXinds = []
eYinds = []
eAinds = []
eRinds = []
eXinds_f = []
eYinds_f = []
eAinds_f = []
eRinds_f = []
pulse_eng_ind = 0
photon_eng_ind = 0
self.acqiris_data_wf[2:7] = self.acqiris_data_wf[2:7] - np.mean(self.acqiris_data_wf[2:7,self.acqiris_data_wt[6,:]>10000], axis=1)[:,np.newaxis]
self.acqiris_data_wf[self.t_channel] = -self.acqiris_data_wf[self.t_channel]
t_peaks = np.array(self.PeakFinderT.cfd(self.acqiris_data_wf[self.t_channel],self.acqiris_data_wt[self.t_channel]))
x1_peaks = np.array(self.PeakFinderX.cfd(self.acqiris_data_wf[self.x1_channel],self.acqiris_data_wt[self.x1_channel]))
x2_peaks = np.array(self.PeakFinderX.cfd(self.acqiris_data_wf[self.x2_channel],self.acqiris_data_wt[self.x2_channel]))
y1_peaks = np.array(self.PeakFinderY.cfd(self.acqiris_data_wf[self.y1_channel],self.acqiris_data_wt[self.y1_channel]))
y2_peaks = np.array(self.PeakFinderY.cfd(self.acqiris_data_wf[self.y2_channel],self.acqiris_data_wt[self.y2_channel]))
e_peaks = self.alg.peak_finder_v4r2(self.eImage, thr_low=self.thr_low, thr_high=self.thr_high, rank=self.rank, r0=self.r0, dr=self.dr)
self.num_events_all += 1
if (0==1) and self.num_events_all == 60:
self.results_dict['acq'] = (self.acqiris_data_wt, self.acqiris_data_wf)
self.results_dict['t_peaks'] = t_peaks
self.results_dict['x1_peaks'] = x1_peaks
self.results_dict['x2_peaks'] = x2_peaks
self.results_dict['y1_peaks'] = y1_peaks
self.results_dict['y2_peaks'] = y2_peaks
else:
self.results_dict['acq'] = None
for t_peak in t_peaks:
MCPinds.append(next((MCP_ind for MCP_ind, MCP_ele in enumerate(self.Taxis) if MCP_ele > t_peak),0))
ion_hits = self.HitFinder.FindHits(t_peaks, x1_peaks, x2_peaks, y1_peaks, y2_peaks)
if (0==1):
self.results_dict['ion_hits'] = ion_hits
eXs = e_peaks[:,1].astype(int)
eYs = e_peaks[:,2].astype(int)
eRadius, eAngle =self.cart2polar(eXs, eYs)
for ion_hit in ion_hits:
Tofinds.append(next((Tof_ind for Tof_ind, Tof_ele in enumerate(self.Taxis) if Tof_ele > ion_hit[0]),0))
Xinds.append(next((X_ind for X_ind, X_ele in enumerate(self.Xaxis) if X_ele > ion_hit[1]),0))
Yinds.append(next((Y_ind for Y_ind, Y_ele in enumerate(self.Yaxis) if Y_ele > ion_hit[2]),0))
for e_ind in range(len(eXs)):
tempRind = next((eR_ind for eR_ind, eR_ele in enumerate(self.eRaxis) if eR_ele > eRadius[e_ind]),0)
tempAind = next((eA_ind for eA_ind, eA_ele in enumerate(self.eAaxis) if eA_ele > eAngle[e_ind]),0)
tempXind = next((eX_ind for eX_ind, eX_ele in enumerate(self.eXaxis) if eX_ele > eXs[e_ind]),0)
tempYind = next((eY_ind for eY_ind, eY_ele in enumerate(self.eYaxis) if eY_ele > eYs[e_ind]),0)
eRinds.append(tempRind)
eAinds.append(tempAind)
eXinds.append(tempXind)
eYinds.append(tempYind)
if not (eRadius[e_ind] > 172.2 and eRadius[e_ind] < 361 and eAngle[e_ind] > -2.5 and eAngle[e_ind] < -0.8):
if eRadius[e_ind] > 100:
eRinds_f.append(tempRind)
eAinds_f.append(tempAind)
eXinds_f.append(tempXind)
eYinds_f.append(tempYind)
pulse_eng_ind = next((pls_eng_ind for pls_eng_ind, pls_eng_ele in enumerate(self.pulse_eng_axis) if pls_eng_ele > self.pulse_eng),0)
photon_eng_ind = next((pho_eng_ind for pho_eng_ind, pho_eng_ele in enumerate(self.photon_eng_axis) if pho_eng_ele > self.photon_eng),0)
self.results_dict['t_peaks'] = MCPinds
self.results_dict['ion'] = {'Xinds': Xinds, 'Yinds': Yinds, 'Tofinds': Tofinds}
self.results_dict['e'] = {'eXinds': eXinds, 'eYinds': eYinds, 'eRinds': eRinds, 'eAinds': eAinds, 'eXinds_f': eXinds_f, 'eYinds_f': eYinds_f, 'eRinds_f': eRinds_f, 'eAinds_f': eAinds_f}
self.results_dict['beam'] = {'pulse_eng_ind': pulse_eng_ind, 'photon_eng_ind': photon_eng_ind}
return self.results_dict, self.mpi_rank
def collect_data_offline(self, new):
self.collected_data = {}
self.collected_data, _ = new
if len(self.collected_data['ion']['Xinds']) > 0 and len(self.collected_data['e']['Xinds']) > 0 and len(self.collected_data['beam']['pulse_eng_ind']) > 0 and len(self.collected_data['beam']['photon_eng_ind']) > 0:
self.num_events += 1
if self.num_events % self.speed_rep_int == 0:
self.time = time.time()
print('Processed: {0} in {1:.2f} seconds ({2:.2f} Hz)'.format(
self.num_events,
self.time - self.old_time,
float(self.speed_rep_int)/float(self.time-self.old_time)))
sys.stdout.flush()
self.old_time = self.time
if self.num_events == self.output_params['max_events'] :
self.shutdown(msg='maximum number of events reached.')
return
def collect_data(self, new):
self.collected_data = {}
self.collected_data, _ = new
if len(self.collected_data['ion']['Xinds']) > 0 and len(self.collected_data['e']['Xinds']) > 0 and len(self.collected_data['beam']['pulse_eng_ind']) > 0 and len(self.collected_data['beam']['photon_eng_ind']) > 0:
self.zmq_publish.send(b'coin_data', zmq.SNDMORE)
self.zmq_publish.send_pyobj(self.collected_data)
self.num_events += 1
if self.num_events % self.speed_rep_int == 0:
self.time = time.time()
print('Processed: {0} in {1:.2f} seconds ({2:.2f} Hz)'.format(
self.num_events,
self.time - self.old_time,
float(self.speed_rep_int)/float(self.time-self.old_time)))
sys.stdout.flush()
self.old_time = self.time
if self.num_events == self.output_params['max_events'] :
self.shutdown(msg='maximum number of events reached.')
return
import psana
ds = psana.DataSource('exp=xpptut15:run=54:smd')
det = psana.Detector('cspad')
from ImgAlgos.PyAlgos import PyAlgos
alg = PyAlgos()
alg.set_peak_selection_pars(npix_min=2, npix_max=50, amax_thr=10, atot_thr=20, son_min=5)
hdr = '\nSeg Row Col Npix Amptot'
fmt = '%3d %4d %4d %4d %8.1f'
for nevent,evt in enumerate(ds.events()):
if nevent>=2 : break
nda = det.calib(evt)
if nda is None: continue
peaks = alg.peak_finder_v1(nda, thr_low=5, thr_high=21, radius=5, dr=0.05)
print hdr
for peak in peaks :
seg,row,col,npix,amax,atot = peak[0:6]
print fmt % (seg, row, col, npix, atot)
def __init__(self,exp,run,detname,evt,detector,algorithm,hitParam_alg_npix_min,hitParam_alg_npix_max,
hitParam_alg_amax_thr,hitParam_alg_atot_thr,hitParam_alg_son_min,
streakMask_on,streakMask_sigma,streakMask_width,userMask_path,psanaMask_on,psanaMask_calib,
psanaMask_status,psanaMask_edges,psanaMask_central,psanaMask_unbond,psanaMask_unbondnrs,
medianFilterOn=0, medianRank=5, radialFilterOn=0, distance=0.0, windows=None, **kwargs):
self.exp = exp
self.run = run
self.detname = detname
self.det = detector
self.algorithm = algorithm
self.maxRes = 0
self.npix_min=hitParam_alg_npix_min
self.npix_max=hitParam_alg_npix_max
self.amax_thr=hitParam_alg_amax_thr
self.atot_thr=hitParam_alg_atot_thr
self.son_min=hitParam_alg_son_min
self.streakMask_on = str2bool(streakMask_on)
self.streakMask_sigma = streakMask_sigma
self.streakMask_width = streakMask_width
self.userMask_path = userMask_path
self.psanaMask_on = str2bool(psanaMask_on)
self.psanaMask_calib = str2bool(psanaMask_calib)
self.psanaMask_status = str2bool(psanaMask_status)
self.psanaMask_edges = str2bool(psanaMask_edges)
self.psanaMask_central = str2bool(psanaMask_central)
self.psanaMask_unbond = str2bool(psanaMask_unbond)
self.psanaMask_unbondnrs = str2bool(psanaMask_unbondnrs)
self.medianFilterOn = medianFilterOn
self.medianRank = medianRank
self.radialFilterOn = radialFilterOn
self.distance = distance
self.windows = windows
self.userMask = None
self.psanaMask = None
self.streakMask = None
self.userPsanaMask = None
self.combinedMask = None
# Make user mask
if self.userMask_path is not None:
self.userMask = np.load(self.userMask_path)
# Make psana mask
if self.psanaMask_on:
self.psanaMask = detector.mask(evt, calib=self.psanaMask_calib, status=self.psanaMask_status,
edges=self.psanaMask_edges, central=self.psanaMask_central,
unbond=self.psanaMask_unbond, unbondnbrs=self.psanaMask_unbondnrs)
# Combine userMask and psanaMask
self.userPsanaMask = np.ones_like(self.det.calib(evt))
if self.userMask is not None:
self.userPsanaMask *= self.userMask
if self.psanaMask is not None:
self.userPsanaMask *= self.psanaMask
# Powder of hits and misses
self.powderHits = np.zeros_like(self.userPsanaMask)
self.powderMisses = np.zeros_like(self.userPsanaMask)
self.alg = PyAlgos(windows=self.windows, mask=self.userPsanaMask, pbits=0)
# set peak-selector parameters:
self.alg.set_peak_selection_pars(npix_min=self.npix_min, npix_max=self.npix_max, \
amax_thr=self.amax_thr, atot_thr=self.atot_thr, \
son_min=self.son_min)
# set algorithm specific parameters
if algorithm == 1:
self.hitParam_alg1_thr_low = kwargs["alg1_thr_low"]
self.hitParam_alg1_thr_high = kwargs["alg1_thr_high"]
self.hitParam_alg1_rank = int(kwargs["alg1_rank"])
self.hitParam_alg1_radius = int(kwargs["alg1_radius"])
self.hitParam_alg1_dr = kwargs["alg1_dr"]
elif algorithm == 3:
self.hitParam_alg3_rank = kwargs["alg3_rank"]
self.hitParam_alg3_r0 = int(kwargs["alg3_r0"])
self.hitParam_alg3_dr = kwargs["alg3_dr"]
elif algorithm == 4:
self.hitParam_alg4_thr_low = kwargs["alg4_thr_low"]
self.hitParam_alg4_thr_high = kwargs["alg4_thr_high"]
self.hitParam_alg4_rank = int(kwargs["alg4_rank"])
self.hitParam_alg4_r0 = int(kwargs["alg4_r0"])
self.hitParam_alg4_dr = kwargs["alg4_dr"]
self.maxNumPeaks = 2048
self.StreakMask = myskbeam.StreakMask(self.det, evt, width=self.streakMask_width, sigma=self.streakMask_sigma)
self.cx, self.cy = self.det.point_indexes(evt, pxy_um=(0, 0))
self.iX = np.array(self.det.indexes_x(evt), dtype=np.int64)
self.iY = np.array(self.det.indexes_y(evt), dtype=np.int64)
if len(self.iX.shape) == 2:
self.iX = np.expand_dims(self.iX, axis=0)
self.iY = np.expand_dims(self.iY, axis=0)
# Initialize radial background subtraction
self.setupExperiment()
if self.radialFilterOn:
self.setupRadialBackground()
self.updatePolarizationFactor()
def updateClassification(self):
if self.parent.calib is not None:
if self.parent.mk.streakMaskOn:
self.parent.mk.initMask()
self.parent.mk.streakMask = self.parent.mk.StreakMask.getStreakMaskCalib(self.parent.evt)
if self.parent.mk.streakMask is None:
self.parent.mk.streakMaskAssem = None
else:
self.parent.mk.streakMaskAssem = self.parent.det.image(self.parent.evt, self.parent.mk.streakMask)
self.algInitDone = False
self.parent.mk.displayMask()
# update combined mask
self.parent.mk.combinedMask = np.ones_like(self.parent.calib)
if self.parent.mk.streakMask is not None and self.parent.mk.streakMaskOn is True:
self.parent.mk.combinedMask *= self.parent.mk.streakMask
if self.parent.mk.userMask is not None and self.parent.mk.userMaskOn is True:
self.parent.mk.combinedMask *= self.parent.mk.userMask
if self.parent.mk.psanaMask is not None and self.parent.mk.psanaMaskOn is True:
self.parent.mk.combinedMask *= self.parent.mk.psanaMask
# Peak output (0-16):
# 0 seg
# 1 row
# 2 col
# 3 npix: no. of pixels in the ROI intensities above threshold
# 4 amp_max: max intensity
# 5 amp_tot: sum of intensities
# 6,7: row_cgrav: center of mass
# 8,9: row_sigma
# 10,11,12,13: minimum bounding box
# 14: background
# 15: noise
# 16: signal over noise
if self.algorithm == 0: # No peak algorithm
self.peaks = None
self.drawPeaks()
else:
# Only initialize the hit finder algorithm once
if self.algInitDone is False:
self.windows = None
self.alg = []
self.alg = PyAlgos(windows=self.windows, mask=self.parent.mk.combinedMask, pbits=0)
# set peak-selector parameters:
if self.algorithm == 1:
self.alg.set_peak_selection_pars(npix_min=self.hitParam_alg1_npix_min, npix_max=self.hitParam_alg1_npix_max, \
amax_thr=self.hitParam_alg1_amax_thr, atot_thr=self.hitParam_alg1_atot_thr, \
son_min=self.hitParam_alg1_son_min)
elif self.algorithm == 2:
self.alg.set_peak_selection_pars(npix_min=self.hitParam_alg2_npix_min, npix_max=self.hitParam_alg2_npix_max, \
amax_thr=self.hitParam_alg2_amax_thr, atot_thr=self.hitParam_alg2_atot_thr, \
son_min=self.hitParam_alg2_son_min)
elif self.algorithm == 3:
self.alg.set_peak_selection_pars(npix_min=self.hitParam_alg3_npix_min, npix_max=self.hitParam_alg3_npix_max, \
amax_thr=self.hitParam_alg3_amax_thr, atot_thr=self.hitParam_alg3_atot_thr, \
son_min=self.hitParam_alg3_son_min)
elif self.algorithm == 4:
self.alg.set_peak_selection_pars(npix_min=self.hitParam_alg4_npix_min, npix_max=self.hitParam_alg4_npix_max, \
amax_thr=self.hitParam_alg4_amax_thr, atot_thr=self.hitParam_alg4_atot_thr, \
son_min=self.hitParam_alg4_son_min)
self.algInitDone = True
self.parent.calib = self.parent.calib * 1.0 # Neccessary when int is returned
if self.algorithm == 1:
# v1 - aka Droplet Finder - two-threshold peak-finding algorithm in restricted region
# around pixel with maximal intensity.
self.peakRadius = int(self.hitParam_alg1_radius)
self.peaks = self.alg.peak_finder_v4r2(self.parent.calib,
thr_low=self.hitParam_alg1_thr_low,
thr_high=self.hitParam_alg1_thr_high,
rank=int(self.hitParam_alg1_rank),
r0=self.peakRadius,
dr=self.hitParam_alg1_dr)
elif self.algorithm == 2:
# v2 - define peaks for regions of connected pixels above threshold
self.peakRadius = int(self.hitParam_alg2_r0)
self.peaks = self.alg.peak_finder_v2(self.parent.calib, thr=self.hitParam_alg2_thr, r0=self.peakRadius, dr=self.hitParam_alg2_dr)
elif self.algorithm == 3:
self.peakRadius = int(self.hitParam_alg3_r0)
self.peaks = self.alg.peak_finder_v3(self.parent.calib, rank=self.hitParam_alg3_rank, r0=self.peakRadius, dr=self.hitParam_alg3_dr)
elif self.algorithm == 4:
# v4 - aka Droplet Finder - the same as v1, but uses rank and r0 parameters in stead of common radius.
self.peakRadius = int(self.hitParam_alg4_r0)
self.peaks = self.alg.peak_finder_v4(self.parent.calib, thr_low=self.hitParam_alg4_thr_low, thr_high=self.hitParam_alg4_thr_high,
rank=self.hitParam_alg4_rank, r0=self.peakRadius, dr=self.hitParam_alg4_dr)
self.numPeaksFound = self.peaks.shape[0]
if self.parent.args.v >= 1: print "Num peaks found: ", self.numPeaksFound, self.peaks.shape
# update clen
self.parent.geom.updateClen('lcls')
self.parent.index.clearIndexedPeaks()
# Save image and peaks in cheetah cxi file
self.saveCheetahFormat('lcls')
if self.parent.index.showIndexedPeaks: self.parent.index.updateIndex()
self.drawPeaks()
if self.parent.args.v >= 1: print "Done updateClassification"
class PeakFinding(object):
def __init__(self, parent = None):
self.parent = parent
self.d9 = Dock("Peak Finder", size=(1, 1))
## Dock 9: Peak finder
self.w10 = ParameterTree()
self.d9.addWidget(self.w10)
#self.w11 = pg.LayoutWidget()
#self.generatePowderBtn = QtGui.QPushButton('Generate Powder')
#self.launchBtn = QtGui.QPushButton('Launch peak finder')
#self.w11.addWidget(self.launchBtn, row=0,col=0)
#self.w11.addWidget(self.generatePowderBtn, row=0, col=0)
#self.d9.addWidget(self.w11)
self.userUpdate = None
self.doingUpdate = False
# Peak finding
self.hitParam_grp = 'Peak finder'
self.hitParam_showPeaks_str = 'Show peaks found'
self.hitParam_algorithm_str = 'Algorithm'
# algorithm 0
self.hitParam_algorithm0_str = 'None'
# algorithm 1
self.hitParam_alg1_npix_min_str = 'npix_min'
self.hitParam_alg1_npix_max_str = 'npix_max'
self.hitParam_alg1_amax_thr_str = 'amax_thr'
self.hitParam_alg1_atot_thr_str = 'atot_thr'
self.hitParam_alg1_son_min_str = 'son_min'
self.hitParam_algorithm1_str = 'Droplet'
self.hitParam_alg1_thr_low_str = 'thr_low'
self.hitParam_alg1_thr_high_str = 'thr_high'
self.hitParam_alg1_rank_str = 'rank'
self.hitParam_alg1_radius_str = 'radius'
self.hitParam_alg1_dr_str = 'dr'
# algorithm 2
self.hitParam_alg2_npix_min_str = 'npix_min'
self.hitParam_alg2_npix_max_str = 'npix_max'
self.hitParam_alg2_amax_thr_str = 'amax_thr'
self.hitParam_alg2_atot_thr_str = 'atot_thr'
self.hitParam_alg2_son_min_str = 'son_min'
self.hitParam_algorithm2_str = 'FloodFill'
self.hitParam_alg2_thr_str = 'thr'
self.hitParam_alg2_r0_str = 'r0'
self.hitParam_alg2_dr_str = 'dr'
# algorithm 3
self.hitParam_alg3_npix_min_str = 'npix_min'
self.hitParam_alg3_npix_max_str = 'npix_max'
self.hitParam_alg3_amax_thr_str = 'amax_thr'
self.hitParam_alg3_atot_thr_str = 'atot_thr'
self.hitParam_alg3_son_min_str = 'son_min'
self.hitParam_algorithm3_str = 'Ranker'
self.hitParam_alg3_rank_str = 'rank'
self.hitParam_alg3_r0_str = 'r0'
self.hitParam_alg3_dr_str = 'dr'
# algorithm 4
self.hitParam_alg4_npix_min_str = 'npix_min'
self.hitParam_alg4_npix_max_str = 'npix_max'
self.hitParam_alg4_amax_thr_str = 'amax_thr'
self.hitParam_alg4_atot_thr_str = 'atot_thr'
self.hitParam_alg4_son_min_str = 'son_min'
self.hitParam_algorithm4_str = 'iDroplet'
self.hitParam_alg4_thr_low_str = 'thr_low'
self.hitParam_alg4_thr_high_str = 'thr_high'
self.hitParam_alg4_rank_str = 'rank'
self.hitParam_alg4_r0_str = 'radius'
self.hitParam_alg4_dr_str = 'dr'
self.hitParam_outDir_str = 'Output directory'
self.hitParam_runs_str = 'Run(s)'
self.hitParam_queue_str = 'queue'
self.hitParam_cpu_str = 'CPUs'
self.hitParam_psanaq_str = 'psanaq'
self.hitParam_psnehq_str = 'psnehq'
self.hitParam_psfehq_str = 'psfehq'
self.hitParam_psnehprioq_str = 'psnehprioq'
self.hitParam_psfehprioq_str = 'psfehprioq'
self.hitParam_psnehhiprioq_str = 'psnehhiprioq'
self.hitParam_psfehhiprioq_str = 'psfehhiprioq'
self.hitParam_psdebugq_str = 'psdebugq'
self.hitParam_noe_str = 'Number of events to process'
self.hitParam_threshold_str = 'Indexable number of peaks'
self.hitParam_launch_str = 'Launch peak finder'
self.hitParam_extra_str = 'Extra parameters'
self.save_minPeaks_str = 'Minimum number of peaks'
self.save_maxPeaks_str = 'Maximum number of peaks'
self.save_minRes_str = 'Minimum resolution (pixels)'
self.save_sample_str = 'Sample name'
self.showPeaks = True
self.peaks = None
self.numPeaksFound = 0
self.algorithm = 0
self.algInitDone = False
self.peaksMaxRes = 0
self.classify = False
self.hitParam_alg1_npix_min = 2.
self.hitParam_alg1_npix_max = 20.
self.hitParam_alg1_amax_thr = 0.
self.hitParam_alg1_atot_thr = 1000.
self.hitParam_alg1_son_min = 7.
self.hitParam_alg1_thr_low = 250.
self.hitParam_alg1_thr_high = 600.
self.hitParam_alg1_rank = 2
self.hitParam_alg1_radius = 2
self.hitParam_alg1_dr = 1
# self.hitParam_alg2_npix_min = 1.
# self.hitParam_alg2_npix_max = 5000.
# self.hitParam_alg2_amax_thr = 1.
# self.hitParam_alg2_atot_thr = 1.
# self.hitParam_alg2_son_min = 1.
# self.hitParam_alg2_thr = 10.
# self.hitParam_alg2_r0 = 1.
# self.hitParam_alg2_dr = 0.05
# self.hitParam_alg3_npix_min = 5.
# self.hitParam_alg3_npix_max = 5000.
# self.hitParam_alg3_amax_thr = 0.
# self.hitParam_alg3_atot_thr = 0.
# self.hitParam_alg3_son_min = 4.
# self.hitParam_alg3_rank = 3
# self.hitParam_alg3_r0 = 5.
# self.hitParam_alg3_dr = 0.05
# self.hitParam_alg4_npix_min = 1.
# self.hitParam_alg4_npix_max = 45.
# self.hitParam_alg4_amax_thr = 800.
# self.hitParam_alg4_atot_thr = 0
# self.hitParam_alg4_son_min = 7.
# self.hitParam_alg4_thr_low = 200.
# self.hitParam_alg4_thr_high = self.hitParam_alg1_thr_high
# self.hitParam_alg4_rank = 3
# self.hitParam_alg4_r0 = 2
# self.hitParam_alg4_dr = 1
self.hitParam_outDir = self.parent.psocakeDir
self.hitParam_outDir_overridden = False
self.hitParam_runs = ''
self.hitParam_queue = self.hitParam_psanaq_str
self.hitParam_cpus = 24
self.hitParam_noe = -1
self.hitParam_threshold = 15 # usually crystals with less than 15 peaks are not indexable
self.minPeaks = 15
self.maxPeaks = 2048
self.minRes = -1
self.sample = 'sample'
self.profile = 0
self.hitParam_extra = ''
self.params = [
{'name': self.hitParam_grp, 'type': 'group', 'children': [
{'name': self.hitParam_showPeaks_str, 'type': 'bool', 'value': self.showPeaks,
'tip': "Show peaks found shot-to-shot"},
{'name': self.hitParam_algorithm_str, 'type': 'list', 'values': {self.hitParam_algorithm1_str: 1,
self.hitParam_algorithm0_str: 0},
'value': self.algorithm},
{'name': self.hitParam_algorithm1_str, 'visible': True, 'expanded': False, 'type': 'str', 'value': "",
'readonly': True, 'children': [
{'name': self.hitParam_alg1_npix_min_str, 'type': 'float', 'value': self.hitParam_alg1_npix_min,
'tip': "Only keep the peak if number of pixels above thr_low is above this value"},
{'name': self.hitParam_alg1_npix_max_str, 'type': 'float', 'value': self.hitParam_alg1_npix_max,
'tip': "Only keep the peak if number of pixels above thr_low is below this value"},
{'name': self.hitParam_alg1_amax_thr_str, 'type': 'float', 'value': self.hitParam_alg1_amax_thr,
'tip': "Only keep the peak if max value is above this value"},
{'name': self.hitParam_alg1_atot_thr_str, 'type': 'float', 'value': self.hitParam_alg1_atot_thr,
'tip': "Only keep the peak if integral inside region of interest is above this value"},
{'name': self.hitParam_alg1_son_min_str, 'type': 'float', 'value': self.hitParam_alg1_son_min,
'tip': "Only keep the peak if signal-over-noise is above this value"},
{'name': self.hitParam_alg1_thr_low_str, 'type': 'float', 'value': self.hitParam_alg1_thr_low,
'tip': "Grow a seed peak if above this value"},
{'name': self.hitParam_alg1_thr_high_str, 'type': 'float', 'value': self.hitParam_alg1_thr_high,
'tip': "Start a seed peak if above this value"},
{'name': self.hitParam_alg1_rank_str, 'type': 'int', 'value': self.hitParam_alg1_rank,
'tip': "region of integration is a square, (2r+1)x(2r+1)"},
{'name': self.hitParam_alg1_radius_str, 'type': 'int', 'value': self.hitParam_alg1_radius,
'tip': "region inside the region of interest"},
{'name': self.hitParam_alg1_dr_str, 'type': 'float', 'value': self.hitParam_alg1_dr,
'tip': "background region outside the region of interest"},
]},
{'name': self.save_minPeaks_str, 'type': 'int', 'value': self.minPeaks,
'tip': "Index only if there are more Bragg peaks found"},
{'name': self.save_maxPeaks_str, 'type': 'int', 'value': self.maxPeaks,
'tip': "Index only if there are less Bragg peaks found"},
{'name': self.save_minRes_str, 'type': 'int', 'value': self.minRes,
'tip': "Index only if Bragg peak resolution is at least this"},
{'name': self.save_sample_str, 'type': 'str', 'value': self.sample,
'tip': "Sample name saved inside cxi"},
{'name': self.hitParam_outDir_str, 'type': 'str', 'value': self.hitParam_outDir},
{'name': self.hitParam_runs_str, 'type': 'str', 'value': self.hitParam_runs},
{'name': self.hitParam_queue_str, 'type': 'list', 'values': {self.hitParam_psfehhiprioq_str: 'psfehhiprioq',
self.hitParam_psnehhiprioq_str: 'psnehhiprioq',
self.hitParam_psfehprioq_str: 'psfehprioq',
self.hitParam_psnehprioq_str: 'psnehprioq',
self.hitParam_psfehq_str: 'psfehq',
self.hitParam_psnehq_str: 'psnehq',
self.hitParam_psanaq_str: 'psanaq',
self.hitParam_psdebugq_str: 'psdebugq'},
'value': self.hitParam_queue, 'tip': "Choose queue"},
{'name': self.hitParam_cpu_str, 'type': 'int', 'value': self.hitParam_cpus},
{'name': self.hitParam_noe_str, 'type': 'int', 'value': self.hitParam_noe,
'tip': "number of events to process, default=-1 means process all events"},
{'name': self.hitParam_extra_str, 'type': 'str', 'value': self.hitParam_extra, 'tip': "Extra peak finding flags"},
{'name': self.hitParam_launch_str, 'type': 'action'},
]},
]
self.p3 = Parameter.create(name='paramsPeakFinder', type='group', \
children=self.params, expanded=True)
self.w10.setParameters(self.p3, showTop=False)
self.p3.sigTreeStateChanged.connect(self.change)
#self.parent.connect(self.launchBtn, QtCore.SIGNAL("clicked()"), self.findPeaks)
def digestRunList(self, runList):
runsToDo = []
if not runList:
print "Run(s) is empty. Please type in the run number(s)."
return runsToDo
runLists = str(runList).split(",")
for list in runLists:
temp = list.split(":")
if len(temp) == 2:
for i in np.arange(int(temp[0]),int(temp[1])+1):
runsToDo.append(i)
elif len(temp) == 1:
runsToDo.append(int(temp[0]))
return runsToDo
def updateParam(self):
if self.userUpdate is None:
if self.parent.psocakeRunDir is not None:
peakParamFname = self.parent.psocakeRunDir + '/peakParam.json'
if os.path.exists(peakParamFname):
with open(peakParamFname) as infile:
d = json.load(infile)
if d[self.hitParam_algorithm_str] == 1:
# Update variables
try:
self.hitParam_alg1_npix_min = d[self.hitParam_alg1_npix_min_str]
self.hitParam_alg1_npix_max = d[self.hitParam_alg1_npix_max_str]
self.hitParam_alg1_amax_thr = d[self.hitParam_alg1_amax_thr_str]
self.hitParam_alg1_atot_thr = d[self.hitParam_alg1_atot_thr_str]
self.hitParam_alg1_son_min = d[self.hitParam_alg1_son_min_str]
self.hitParam_alg1_thr_low = d[self.hitParam_alg1_thr_low_str]
self.hitParam_alg1_thr_high = d[self.hitParam_alg1_thr_high_str]
self.hitParam_alg1_rank = int(d[self.hitParam_alg1_rank_str])
self.hitParam_alg1_radius = int(d[self.hitParam_alg1_radius_str])
self.hitParam_alg1_dr = d[self.hitParam_alg1_dr_str]
# Update GUI
self.doingUpdate = True
#self.p3.param(self.hitParam_grp, self.hitParam_algorithm_str).setValue(self.algorithm)
self.p3.param(self.hitParam_grp, self.hitParam_algorithm1_str, self.hitParam_alg1_npix_min_str).setValue(
self.hitParam_alg1_npix_min)
self.p3.param(self.hitParam_grp, self.hitParam_algorithm1_str, self.hitParam_alg1_npix_max_str).setValue(
self.hitParam_alg1_npix_max)
self.p3.param(self.hitParam_grp, self.hitParam_algorithm1_str, self.hitParam_alg1_amax_thr_str).setValue(
self.hitParam_alg1_amax_thr)
self.p3.param(self.hitParam_grp, self.hitParam_algorithm1_str, self.hitParam_alg1_atot_thr_str).setValue(
self.hitParam_alg1_atot_thr)
self.p3.param(self.hitParam_grp, self.hitParam_algorithm1_str, self.hitParam_alg1_son_min_str).setValue(
self.hitParam_alg1_son_min)
self.p3.param(self.hitParam_grp, self.hitParam_algorithm1_str, self.hitParam_alg1_thr_low_str).setValue(
self.hitParam_alg1_thr_low)
self.p3.param(self.hitParam_grp, self.hitParam_algorithm1_str, self.hitParam_alg1_thr_high_str).setValue(
self.hitParam_alg1_thr_high)
self.p3.param(self.hitParam_grp, self.hitParam_algorithm1_str, self.hitParam_alg1_rank_str).setValue(
self.hitParam_alg1_rank)
self.p3.param(self.hitParam_grp, self.hitParam_algorithm1_str, self.hitParam_alg1_radius_str).setValue(
self.hitParam_alg1_radius)
self.doingUpdate = False
self.p3.param(self.hitParam_grp, self.hitParam_algorithm1_str, self.hitParam_alg1_dr_str).setValue(
self.hitParam_alg1_dr)
except:
pass
def writeStatus(self, fname, d):
json.dump(d, open(fname, 'w'))
# Launch peak finding
def findPeaks(self):
self.parent.thread.append(LaunchPeakFinder.LaunchPeakFinder(self.parent)) # send parent parameters with self
self.parent.thread[self.parent.threadCounter].launch(self.parent.experimentName, self.parent.detInfo)
self.parent.threadCounter+=1
# Save peak finding parameters
runsToDo = self.digestRunList(self.hitParam_runs)
for run in runsToDo:
peakParamFname = self.parent.psocakeDir+'/r'+str(run).zfill(4)+'/peakParam.json'
d = {self.hitParam_algorithm_str: self.algorithm,
self.hitParam_alg1_npix_min_str: self.hitParam_alg1_npix_min,
self.hitParam_alg1_npix_max_str: self.hitParam_alg1_npix_max,
self.hitParam_alg1_amax_thr_str: self.hitParam_alg1_amax_thr,
self.hitParam_alg1_atot_thr_str: self.hitParam_alg1_atot_thr,
self.hitParam_alg1_son_min_str: self.hitParam_alg1_son_min,
self.hitParam_alg1_thr_low_str: self.hitParam_alg1_thr_low,
self.hitParam_alg1_thr_high_str: self.hitParam_alg1_thr_high,
self.hitParam_alg1_rank_str: self.hitParam_alg1_rank,
self.hitParam_alg1_radius_str: self.hitParam_alg1_radius,
self.hitParam_alg1_dr_str: self.hitParam_alg1_dr}
self.writeStatus(peakParamFname, d)
# If anything changes in the parameter tree, print a message
def change(self, panel, changes):
for param, change, data in changes:
path = panel.childPath(param)
if self.parent.args.v >= 1:
print(' path: %s' % path)
print(' change: %s' % change)
print(' data: %s' % str(data))
print(' ----------')
self.paramUpdate(path, change, data)
##############################
# Mandatory parameter update #
##############################
def paramUpdate(self, path, change, data):
if path[0] == self.hitParam_grp:
if path[1] == self.hitParam_algorithm_str:
self.algInitDone = False
self.updateAlgorithm(data)
elif path[1] == self.hitParam_showPeaks_str:
self.showPeaks = data
self.drawPeaks()
elif path[1] == self.hitParam_outDir_str:
self.hitParam_outDir = data
self.hitParam_outDir_overridden = True
elif path[1] == self.hitParam_runs_str:
self.hitParam_runs = data
elif path[1] == self.hitParam_queue_str:
self.hitParam_queue = data
elif path[1] == self.hitParam_cpu_str:
self.hitParam_cpus = data
elif path[1] == self.hitParam_noe_str:
self.hitParam_noe = data
elif path[1] == self.hitParam_threshold_str:
self.hitParam_threshold = data
elif path[1] == self.hitParam_launch_str:
self.findPeaks()
elif path[1] == self.save_minPeaks_str:
self.minPeaks = data
elif path[1] == self.save_maxPeaks_str:
self.maxPeaks = data
elif path[1] == self.save_minRes_str:
self.minRes = data
elif path[1] == self.save_sample_str:
self.sample = data
elif path[1] == self.hitParam_extra_str:
self.hitParam_extra = data
elif path[2] == self.hitParam_alg1_npix_min_str and path[1] == self.hitParam_algorithm1_str:
self.hitParam_alg1_npix_min = data
self.algInitDone = False
self.userUpdate = True
if self.showPeaks and self.doingUpdate is False:
self.updateClassification()
elif path[2] == self.hitParam_alg1_npix_max_str and path[1] == self.hitParam_algorithm1_str:
self.hitParam_alg1_npix_max = data
self.algInitDone = False
self.userUpdate = True
if self.showPeaks and self.doingUpdate is False:
self.updateClassification()
elif path[2] == self.hitParam_alg1_amax_thr_str and path[1] == self.hitParam_algorithm1_str:
self.hitParam_alg1_amax_thr = data
self.algInitDone = False
self.userUpdate = True
if self.showPeaks and self.doingUpdate is False:
self.updateClassification()
elif path[2] == self.hitParam_alg1_atot_thr_str and path[1] == self.hitParam_algorithm1_str:
self.hitParam_alg1_atot_thr = data
self.algInitDone = False
self.userUpdate = True
if self.showPeaks and self.doingUpdate is False:
self.updateClassification()
elif path[2] == self.hitParam_alg1_son_min_str and path[1] == self.hitParam_algorithm1_str:
self.hitParam_alg1_son_min = data
self.algInitDone = False
self.userUpdate = True
if self.showPeaks and self.doingUpdate is False:
self.updateClassification()
elif path[2] == self.hitParam_alg1_thr_low_str and path[1] == self.hitParam_algorithm1_str:
self.hitParam_alg1_thr_low = data
self.algInitDone = False
self.userUpdate = True
if self.showPeaks and self.doingUpdate is False:
self.updateClassification()
elif path[2] == self.hitParam_alg1_thr_high_str and path[1] == self.hitParam_algorithm1_str:
self.hitParam_alg1_thr_high = data
self.algInitDone = False
self.userUpdate = True
if self.showPeaks and self.doingUpdate is False:
self.updateClassification()
elif path[2] == self.hitParam_alg1_rank_str and path[1] == self.hitParam_algorithm1_str:
self.hitParam_alg1_rank = data
self.algInitDone = False
self.userUpdate = True
if self.showPeaks and self.doingUpdate is False:
self.updateClassification()
elif path[2] == self.hitParam_alg1_radius_str and path[1] == self.hitParam_algorithm1_str:
self.hitParam_alg1_radius = data
self.algInitDone = False
self.userUpdate = True
if self.showPeaks and self.doingUpdate is False:
self.updateClassification()
elif path[2] == self.hitParam_alg1_dr_str and path[1] == self.hitParam_algorithm1_str:
self.hitParam_alg1_dr = data
self.algInitDone = False
self.userUpdate = True
if self.showPeaks and self.doingUpdate is False:
self.updateClassification()
def updateAlgorithm(self, data):
self.algorithm = data
self.algInitDone = False
self.updateClassification()
if self.parent.args.v >= 1: print "##### Done updateAlgorithm: ", self.algorithm
def saveCheetahFormat(self, arg):
if arg == 'lcls':
if 'cspad' in self.parent.detInfo.lower() and 'cxi' in self.parent.experimentName:
dim0 = 8 * 185
dim1 = 4 * 388
elif 'rayonix' in self.parent.detInfo.lower() and 'mfx' in self.parent.experimentName:
dim0 = 1920 #FIXME: rayonix can be binned
dim1 = 1920
elif 'rayonix' in self.parent.detInfo.lower() and 'xpp' in self.parent.experimentName:
dim0 = 1920 #FIXME: rayonix can be binned
dim1 = 1920
else:
dim0 = 0
dim1 = 0
if dim0 > 0:
maxNumPeaks = 2048
if self.parent.index.hiddenCXI is not None:
myHdf5 = h5py.File(self.parent.index.hiddenCXI, 'w')
grpName = "/entry_1/result_1"
dset_nPeaks = "/nPeaks"
dset_posX = "/peakXPosRaw"
dset_posY = "/peakYPosRaw"
dset_atot = "/peakTotalIntensity"
if grpName in myHdf5:
del myHdf5[grpName]
grp = myHdf5.create_group(grpName)
myHdf5.create_dataset(grpName + dset_nPeaks, (1,), dtype='int')
myHdf5.create_dataset(grpName + dset_posX, (1, maxNumPeaks), dtype='float32', chunks=(1, maxNumPeaks))
myHdf5.create_dataset(grpName + dset_posY, (1, maxNumPeaks), dtype='float32', chunks=(1, maxNumPeaks))
myHdf5.create_dataset(grpName + dset_atot, (1, maxNumPeaks), dtype='float32', chunks=(1, maxNumPeaks))
myHdf5.create_dataset("/LCLS/detector_1/EncoderValue", (1,), dtype=float)
myHdf5.create_dataset("/LCLS/photon_energy_eV", (1,), dtype=float)
dset = myHdf5.create_dataset("/entry_1/data_1/data", (1, dim0, dim1), dtype=float)
# Convert calib image to cheetah image
img = np.zeros((dim0, dim1))
counter = 0
if 'cspad' in self.parent.detInfo.lower() and 'cxi' in self.parent.experimentName:
for quad in range(4):
for seg in range(8):
img[seg * 185:(seg + 1) * 185, quad * 388:(quad + 1) * 388] = self.parent.calib[counter, :, :]
counter += 1
elif 'rayonix' in self.parent.detInfo.lower() and 'mfx' in self.parent.experimentName:
img = self.parent.calib[:, :] # psana format
elif 'rayonix' in self.parent.detInfo.lower() and 'xpp' in self.parent.experimentName:
img = self.parent.calib[:, :] # psana format
else:
print "saveCheetahFormat not implemented"
peaks = self.peaks.copy()
nPeaks = peaks.shape[0]
if nPeaks > maxNumPeaks:
peaks = peaks[:maxNumPeaks]
nPeaks = maxNumPeaks
for i, peak in enumerate(peaks):
seg, row, col, npix, amax, atot, rcent, ccent, rsigma, csigma, rmin, rmax, cmin, cmax, bkgd, rms, son = peak[0:17]
if 'cspad' in self.parent.detInfo.lower() and 'cxi' in self.parent.experimentName:
cheetahRow, cheetahCol = self.convert_peaks_to_cheetah(seg, row, col)
myHdf5[grpName + dset_posX][0, i] = cheetahCol
myHdf5[grpName + dset_posY][0, i] = cheetahRow
myHdf5[grpName + dset_atot][0, i] = atot
elif 'rayonix' in self.parent.detInfo.lower() and 'mfx' in self.parent.experimentName:
myHdf5[grpName + dset_posX][0, i] = col
myHdf5[grpName + dset_posY][0, i] = row
myHdf5[grpName + dset_atot][0, i] = atot
elif 'rayonix' in self.parent.detInfo.lower() and 'xpp' in self.parent.experimentName:
myHdf5[grpName + dset_posX][0, i] = col
myHdf5[grpName + dset_posY][0, i] = row
myHdf5[grpName + dset_atot][0, i] = atot
myHdf5[grpName + dset_nPeaks][0] = nPeaks
if self.parent.args.v >= 1: print "hiddenCXI clen (mm): ", self.parent.clen * 1000.
myHdf5["/LCLS/detector_1/EncoderValue"][0] = self.parent.clen * 1000. # mm
myHdf5["/LCLS/photon_energy_eV"][0] = self.parent.photonEnergy
dset[0, :, :] = img
myHdf5.close()
def updateClassification(self):
if self.parent.calib is not None:
if self.parent.mk.streakMaskOn:
self.parent.mk.initMask()
self.parent.mk.streakMask = self.parent.mk.StreakMask.getStreakMaskCalib(self.parent.evt)
if self.parent.mk.streakMask is None:
self.parent.mk.streakMaskAssem = None
else:
self.parent.mk.streakMaskAssem = self.parent.det.image(self.parent.evt, self.parent.mk.streakMask)
self.algInitDone = False
self.parent.mk.displayMask()
# update combined mask
self.parent.mk.combinedMask = np.ones_like(self.parent.calib)
if self.parent.mk.streakMask is not None and self.parent.mk.streakMaskOn is True:
self.parent.mk.combinedMask *= self.parent.mk.streakMask
if self.parent.mk.userMask is not None and self.parent.mk.userMaskOn is True:
self.parent.mk.combinedMask *= self.parent.mk.userMask
if self.parent.mk.psanaMask is not None and self.parent.mk.psanaMaskOn is True:
self.parent.mk.combinedMask *= self.parent.mk.psanaMask
# Peak output (0-16):
# 0 seg
# 1 row
# 2 col
# 3 npix: no. of pixels in the ROI intensities above threshold
# 4 amp_max: max intensity
# 5 amp_tot: sum of intensities
# 6,7: row_cgrav: center of mass
# 8,9: row_sigma
# 10,11,12,13: minimum bounding box
# 14: background
# 15: noise
# 16: signal over noise
if self.algorithm == 0: # No peak algorithm
self.peaks = None
self.drawPeaks()
else:
# Only initialize the hit finder algorithm once
if self.algInitDone is False:
self.windows = None
self.alg = []
self.alg = PyAlgos(windows=self.windows, mask=self.parent.mk.combinedMask, pbits=0)
# set peak-selector parameters:
if self.algorithm == 1:
self.alg.set_peak_selection_pars(npix_min=self.hitParam_alg1_npix_min, npix_max=self.hitParam_alg1_npix_max, \
amax_thr=self.hitParam_alg1_amax_thr, atot_thr=self.hitParam_alg1_atot_thr, \
son_min=self.hitParam_alg1_son_min)
elif self.algorithm == 2:
self.alg.set_peak_selection_pars(npix_min=self.hitParam_alg2_npix_min, npix_max=self.hitParam_alg2_npix_max, \
amax_thr=self.hitParam_alg2_amax_thr, atot_thr=self.hitParam_alg2_atot_thr, \
son_min=self.hitParam_alg2_son_min)
elif self.algorithm == 3:
self.alg.set_peak_selection_pars(npix_min=self.hitParam_alg3_npix_min, npix_max=self.hitParam_alg3_npix_max, \
amax_thr=self.hitParam_alg3_amax_thr, atot_thr=self.hitParam_alg3_atot_thr, \
son_min=self.hitParam_alg3_son_min)
elif self.algorithm == 4:
self.alg.set_peak_selection_pars(npix_min=self.hitParam_alg4_npix_min, npix_max=self.hitParam_alg4_npix_max, \
amax_thr=self.hitParam_alg4_amax_thr, atot_thr=self.hitParam_alg4_atot_thr, \
son_min=self.hitParam_alg4_son_min)
self.algInitDone = True
self.parent.calib = self.parent.calib * 1.0 # Neccessary when int is returned
if self.algorithm == 1:
# v1 - aka Droplet Finder - two-threshold peak-finding algorithm in restricted region
# around pixel with maximal intensity.
self.peakRadius = int(self.hitParam_alg1_radius)
self.peaks = self.alg.peak_finder_v4r2(self.parent.calib,
thr_low=self.hitParam_alg1_thr_low,
thr_high=self.hitParam_alg1_thr_high,
rank=int(self.hitParam_alg1_rank),
r0=self.peakRadius,
dr=self.hitParam_alg1_dr)
elif self.algorithm == 2:
# v2 - define peaks for regions of connected pixels above threshold
self.peakRadius = int(self.hitParam_alg2_r0)
self.peaks = self.alg.peak_finder_v2(self.parent.calib, thr=self.hitParam_alg2_thr, r0=self.peakRadius, dr=self.hitParam_alg2_dr)
elif self.algorithm == 3:
self.peakRadius = int(self.hitParam_alg3_r0)
self.peaks = self.alg.peak_finder_v3(self.parent.calib, rank=self.hitParam_alg3_rank, r0=self.peakRadius, dr=self.hitParam_alg3_dr)
elif self.algorithm == 4:
# v4 - aka Droplet Finder - the same as v1, but uses rank and r0 parameters in stead of common radius.
self.peakRadius = int(self.hitParam_alg4_r0)
self.peaks = self.alg.peak_finder_v4(self.parent.calib, thr_low=self.hitParam_alg4_thr_low, thr_high=self.hitParam_alg4_thr_high,
rank=self.hitParam_alg4_rank, r0=self.peakRadius, dr=self.hitParam_alg4_dr)
self.numPeaksFound = self.peaks.shape[0]
if self.parent.args.v >= 1: print "Num peaks found: ", self.numPeaksFound, self.peaks.shape
# update clen
self.parent.geom.updateClen('lcls')
self.parent.index.clearIndexedPeaks()
# Save image and peaks in cheetah cxi file
self.saveCheetahFormat('lcls')
if self.parent.index.showIndexedPeaks: self.parent.index.updateIndex()
self.drawPeaks()
if self.parent.args.v >= 1: print "Done updateClassification"
def convert_peaks_to_cheetah(self, s, r, c) :
"""Converts seg, row, col assuming (32,185,388)
to cheetah 2-d table row and col (8*185, 4*388)
"""
segs, rows, cols = (32,185,388)
row2d = (int(s)%8) * rows + int(r) # where s%8 is a segment in quad number [0,7]
col2d = (int(s)/8) * cols + int(c) # where s/8 is a quad number [0,3]
return row2d, col2d
def getMaxRes(self, posX, posY, centerX, centerY):
maxRes = np.max(np.sqrt((posX-centerX)**2 + (posY-centerY)**2))
if self.parent.args.v >= 1: print "maxRes: ", maxRes
return maxRes # in pixels
def drawPeaks(self):
self.parent.img.clearPeakMessage()
if self.showPeaks:
if self.peaks is not None and self.numPeaksFound > 0:
self.ix = self.parent.det.indexes_x(self.parent.evt)
self.iy = self.parent.det.indexes_y(self.parent.evt)
if self.ix is None:
(_, dim0, dim1) = self.parent.calib.shape
self.iy = np.tile(np.arange(dim0),[dim1, 1])
self.ix = np.transpose(self.iy)
self.iX = np.array(self.ix, dtype=np.int64)
self.iY = np.array(self.iy, dtype=np.int64)
if len(self.iX.shape)==2:
self.iX = np.expand_dims(self.iX,axis=0)
self.iY = np.expand_dims(self.iY,axis=0)
cenX = self.iX[np.array(self.peaks[:,0],dtype=np.int64),np.array(self.peaks[:,1],dtype=np.int64),np.array(self.peaks[:,2],dtype=np.int64)] + 0.5
cenY = self.iY[np.array(self.peaks[:,0],dtype=np.int64),np.array(self.peaks[:,1],dtype=np.int64),np.array(self.peaks[:,2],dtype=np.int64)] + 0.5
self.peaksMaxRes = self.getMaxRes(cenX, cenY, self.parent.cx, self.parent.cy)
diameter = self.peakRadius*2+1
self.parent.img.peak_feature.setData(cenX, cenY, symbol='s', \
size=diameter, brush=(255,255,255,0), \
pen=pg.mkPen({'color': "c", 'width': 4}), pxMode=False) #FF0
# Write number of peaks found
xMargin = 5 # pixels
yMargin = 0 # pixels
maxX = np.max(self.ix) + xMargin
maxY = np.max(self.iy) - yMargin
myMessage = '<div style="text-align: center"><span style="color: cyan; font-size: 12pt;">Peaks=' + \
str(self.numPeaksFound) + ' <br>Res=' + str(int(self.peaksMaxRes)) + '<br></span></div>'
self.parent.img.peak_text = pg.TextItem(html=myMessage, anchor=(0, 0))
self.parent.img.w1.getView().addItem(self.parent.img.peak_text)
self.parent.img.peak_text.setPos(maxX, maxY)
else:
self.parent.img.peak_feature.setData([], [], pxMode=False)
self.parent.img.peak_text = pg.TextItem(html='', anchor=(0, 0))
self.parent.img.w1.getView().addItem(self.parent.img.peak_text)
self.parent.img.peak_text.setPos(0,0)
else:
self.parent.img.peak_feature.setData([], [], pxMode=False)
if self.parent.args.v >= 1: print "Done updatePeaks"
winds_bkgd = [(s, 10, 100, 270, 370) for s in (4,12,20,28)] # use part of segments 4 and 20 to subtr bkgd winds_arc = [(s, 0, 185, 0, 388) for s in (0,7,8,15)] winds_equ = [(s, 0, 185, 0, 388) for s in (0,1,9,15,16,17,25,31)] winds_tot = [(s, 0, 185, 0, 388) for s in (0,1,7,8,9,15,16,17,23,24,25,31)] mask_winds_tot = np.zeros(shape_cspad, dtype=np.int16) mask_winds_tot[(0,1,7,8,9,15,16,17,23,24,25,31),:,:] = seg1 #mask_winds_equ[(0,1,9,15,16,17,25,31),:,:] = seg1 #mask_winds_arc[(0,7,8,15),:,:] = seg1 print_arr(winds_arc, 'winds_arc') print_arr_attr(winds_arc, 'winds_arc') alg_arc = PyAlgos(windows=winds_arc, mask=mask_arc, pbits=2) alg_arc.set_peak_selection_pars(npix_min=0, npix_max=1e6, amax_thr=0, atot_thr=0, son_min=10) #alg_arc.set_peak_selection_pars(npix_min=0, npix_max=1e6, amax_thr=0, atot_thr=500, son_min=6) # for v2r1 alg_equ = PyAlgos(windows=winds_equ, mask=mask_equ, pbits=0) alg_equ.set_peak_selection_pars(npix_min=0, npix_max=1e6, amax_thr=0, atot_thr=0, son_min=10) #alg_equ.set_peak_selection_pars(npix_min=0, npix_max=1e6, amax_thr=0, atot_thr=500, son_min=6) # for v2r1 #alg_equ.print_attributes() #alg_equ.print_input_pars() ##----------------------------- xoffset, yoffset = 300, 300 xsize, ysize = 1150, 1150
class MCPGate():
def __init__(self, parent, gateName, tofs, tofl,tofs_dict,tofl_dict):
self.parent = parent
self.gateName = gateName
self.tofs = tofs
self.tofl = tofl
self.tofs_dict = tofs_dict
self.tofl_dict = tofl_dict
self.tofbin = self.parent.monitor_params['t']['bin']
print self.tofs_dict,self.tofl_dict
self.tof_ind = (self.parent.TaxisM>self.tofs) & (self.parent.TaxisM<self.tofl)
self.particle = self.gateName
self.extractE = extractE
self.alg = PyAlgos()
self.alg.set_peak_selection_pars(npix_min=self.parent.npix_min, npix_max=self.parent.npix_max, amax_thr=self.parent.amax_thr, atot_thr=self.parent.atot_thr, son_min=self.parent.son_min)
self.init_vars()
def init_vars(self):
self.P = {}
for pt in ['2n1','n1','n2','n3','n4']:
self.P[pt] = 0
self.temp_irun = -1
self.tempXef1 = []
self.tempYef1 = []
self.tempXef2 = []
self.tempYef2 = []
self.tempErf1 = []
self.tempErf2 = []
self.hists_tof = {}
self.hists_tof_all = {}
for hist_name in self.parent.hist_names_mcp:
if hist_name == 'frag_stat':
x_name = self.parent.monitor_params[hist_name]['x']
x_binnum = self.parent.monitor_params[x_name]['num']
y_name = self.parent.monitor_params[hist_name]['y']
y_binnum = self.parent.monitor_params[y_name]['num']
z_name = self.parent.monitor_params[hist_name]['z']
z_binnum = self.parent.monitor_params[z_name]['num']
m_name = self.parent.monitor_params[hist_name]['m']
m_binnum = self.parent.monitor_params[m_name]['num']
n_name = self.parent.monitor_params[hist_name]['n']
n_binnum = self.parent.monitor_params[n_name]['num']
self.hists_tof[hist_name] = np.zeros([x_binnum, y_binnum, z_binnum, m_binnum, n_binnum])
if self.parent.role == 'master':
self.hists_tof_all[hist_name] = np.zeros([x_binnum, y_binnum, z_binnum, m_binnum, n_binnum])
else:
self.hists_tof_all[hist_name] = None
continue
x_name = self.parent.monitor_params[hist_name]['x']
x_binnum = self.parent.vars_binnum[x_name]
if 'y' in self.parent.monitor_params[hist_name].keys():
y_name = self.parent.monitor_params[hist_name]['y']
y_binnum = self.parent.vars_binnum[y_name]
if 'z' in self.parent.monitor_params[hist_name].keys():
z_name = self.parent.monitor_params[hist_name]['z']
z_binnum = self.parent.vars_binnum[z_name]
self.hists_tof[hist_name] = np.zeros([x_binnum-1, y_binnum-1, z_binnum-1])
if self.parent.role == 'master':
self.hists_tof_all[hist_name] = np.zeros([x_binnum-1, y_binnum-1, z_binnum-1])
else:
self.hists_tof_all[hist_name] = None
else:
self.hists_tof[hist_name] = np.zeros([x_binnum-1, y_binnum-1])
if self.parent.role == 'master':
self.hists_tof_all[hist_name] = np.zeros([x_binnum-1, y_binnum-1])
else:
self.hists_tof_all[hist_name] = None
else:
self.hists_tof[hist_name] = np.zeros([x_binnum-1])
if self.parent.role == 'master':
self.hists_tof_all[hist_name] = np.zeros([x_binnum-1])
else:
self.hists_tof_all[hist_name] = None
def update_electron(self, eAind, eRind, eXind, eYind):
pass
def update_ion(self,Tofele):
for pt in ['2n1','n1','n2','n3','n4']:
if (Tofele > self.tofs_dict[pt] and Tofele < self.tofl_dict[pt]):
self.P[pt] += 1
break
def is_coin(self):
# a = 0
# for pt in ['2n1','n1','n2','n3','n4']:
# a += self.P[pt]
return True
def update_shotinfo(self, pho_ind, pls_ind):
# if self.parent.mpi_rank == 0:
# print 'MCP***********************update',self.P
self.hists_tof['frag_stat'][min(self.P['2n1'],9),min(self.P['n1'],9),min(self.P['n2'],9),min(self.P['n3'],9),min(self.P['n4'],9)] += 1
def reset_coin_var(self):
# if self.parent.mpi_rank == 0:
# print 'MCP**********************reset',self.P
for pt in ['2n1','n1','n2','n3','n4']:
self.P[pt] = 0
def save_var(self, h5f):
grp = h5f.create_group('MCPGate_'+self.gateName)
grp.create_dataset('gateInfo_tofs_tofl_tofbin', data=np.array([self.tofs,self.tofl,self.tofbin]))
for histname in self.parent.hist_names_mcp:
try:
grp.create_dataset(histname,data = self.hists_tof_all[histname])
except Exception as e:
print(histname+' failed to be saved.')
print(e)
def reduce(self):
for histname in self.parent.hist_names_mcp:
try:
self.time_a = time.time()
MPI.COMM_WORLD.Reduce(self.hists_tof[histname],self.hists_tof_all[histname])
self.time_b = time.time()
print(histname+' reduced by '+'Rank {0} in {1:.2f} seconds'.format(self.parent.mpi_rank,self.time_b - self.time_a))
except Exception as e:
print(histname+' failed to be redueced by '+str(self.parent.mpi_rank))
print(e)
class Coin(Workers):
def init_params(self,monitor_params):
self.npix_min, self.npix_max= monitor_params['Opal']['npix_min'],monitor_params['Opal']['npix_max']
self.amax_thr, self.atot_thr, self.son_min = monitor_params['Opal']['amax_thr'], monitor_params['Opal']['atot_thr'], monitor_params['Opal']['son_min']
self.thr_low, self.thr_high = monitor_params['Opal']['thr_low'], monitor_params['Opal']['thr_high']
self.rank, self.r0, self.dr = monitor_params['Opal']['rank'], monitor_params['Opal']['r0'], monitor_params['Opal']['dr']
self.eimg_center_x, self.eimg_center_y = monitor_params['Opal']['eimg_center_x'],monitor_params['Opal']['eimg_center_y']
self.e_radius = monitor_params['Opal']['e_radius']
self.params_gen = monitor_params['General']
self.output_path = monitor_params['OutputLayer']['output_path']
#################################################################
self.output_params = monitor_params['OutputLayer']
self.vars = monitor_params['OutputLayer']['vars'].split(',')
self.hist_names_all = monitor_params['OutputLayer']['hist_names_all'].split(',')
self.hist1_names_all = monitor_params['OutputLayer']['hist1_names_all'].split(',')
#self.vars_min = {}; self.vars_max = {}; self.vars_bin= {};
self.vars_binnum = {}; self.vars_axis={}
self.hists_all = {}
self.hists_all_all = {}
for var in self.vars:
var_min= monitor_params[var]['min']
var_max = monitor_params[var]['max']
var_bin = monitor_params[var]['bin']
var_binnum = int((var_max - var_min)/var_bin)+1
self.vars_binnum[var] = var_binnum
self.vars_axis[var] = np.linspace(var_min, var_max, var_binnum)
for hist_name in self.hist_names_all:
if hist_name=='pipico':
x_name = self.monitor_params[hist_name]['x']
x_binnum = self.vars_binnum[x_name]
y_name = self.monitor_params[hist_name]['y']
y_binnum = self.vars_binnum[y_name]
self.hists_all[hist_name] = np.zeros([x_binnum-2, y_binnum-2])
if self.role == 'master':
self.hists_all_all[hist_name] = np.zeros([x_binnum-2, y_binnum-2])
else:
self.hists_all_all[hist_name] = None
continue
x_name = self.monitor_params[hist_name]['x']
x_binnum = self.vars_binnum[x_name]
if 'y' in self.monitor_params[hist_name].keys():
y_name = self.monitor_params[hist_name]['y']
y_binnum = self.vars_binnum[y_name]
if 'z' in self.monitor_params[hist_name].keys():
z_name = self.monitor_params[hist_name]['z']
z_binnum = self.vars_binnum[z_name]
self.hists_all[hist_name] = np.zeros([x_binnum-1, y_binnum-1, z_binnum-1])
if self.role == 'master':
self.hists_all_all[hist_name] = np.zeros([x_binnum-1, y_binnum-1, z_binnum-1])
else:
self.hists_all_all[hist_name] = None
else:
self.hists_all[hist_name] = np.zeros([x_binnum-1, y_binnum-1])
if self.role == 'master':
self.hists_all_all[hist_name] = np.zeros([x_binnum-1, y_binnum-1])
else:
self.hists_all_all[hist_name] = None
else:
self.hists_all[hist_name] = np.zeros([x_binnum-1])
if self.role == 'master':
self.hists_all_all[hist_name] = np.zeros([x_binnum-1])
else:
self.hists_all_all[hist_name] = None
# print self.hists_all[hist_name].dtype,self.hists_all_all[hist_name].dtype
for hist1_name in self.hist1_names_all:
# print 'hist1_name',hist1_name
self.hists_all[hist1_name] = np.array([0],dtype=np.float64)
if self.role == 'master':
self.hists_all_all[hist1_name] = np.array([0],dtype=np.float64)
else:
self.hists_all_all[hist1_name] = None
self.hist_names_all.append(hist1_name)
# print self.hists_all[hist_name].dtype,self.hists_all_all[hist_name].dtype
def __init__(self, source, monitor_params):
super(Coin, self).__init__(map_func=self.process_data,
reduce_func=self.reduce,save_func=self.save_data,
source=source, monitor_params=monitor_params)
import psana
self.monitor_params = monitor_params
self.init_params(monitor_params)
self.e_cols = self.eimg_center_y - self.e_radius
self.e_coll = self.eimg_center_y + self.e_radius+1
self.e_rows = self.eimg_center_x - self.e_radius
self.e_rowl = self.eimg_center_x + self.e_radius+1
self.e_center_y = self.eimg_center_y
self.e_center_x = self.eimg_center_x
self.mask = np.ones([1024,1024])
self.mask[350:650, 350:650] = 0
self.mask[:, :350] = 0
#self.alg = PyAlgos(mask = self.mask)
self.alg = PyAlgos()
self.alg.set_peak_selection_pars(npix_min=self.npix_min, npix_max=self.npix_max, amax_thr=self.amax_thr, atot_thr=self.atot_thr, son_min=self.son_min)
self.speed_rep_int = self.params_gen['speed_report_interval']
self.old_time = time.time()
self.time = None
print('Starting worker: {0}.'.format(self.mpi_rank))
sys.stdout.flush()
return
def cart2polar_img(self, x, y, intensity):
x1 = x-self.e_center_x
y1 = y-self.e_center_y
r = np.sqrt(x1**2 + y1**2)
angle = np.arctan2(y1, x1)
return r, angle, intensity*r
def cart2polar(self, x, y):
x1 = x-self.e_center_x
y1 = y-self.e_center_y
r = np.sqrt(x1**2 + y1**2)
angle = np.arctan2(y1, x1)
return r, angle
def process_data(self):
self.pls_ind = next((pls_eng_ind for pls_eng_ind, pls_eng_ele in enumerate(self.vars_axis['plseng']) if pls_eng_ele > self.pulse_eng),0)-1
self.pho_ind = next((pho_eng_ind for pho_eng_ind, pho_eng_ele in enumerate(self.vars_axis['phoeng']) if pho_eng_ele > self.photon_eng),0)-1
self.ebm_ind = next((ebm_eng_ind for ebm_eng_ind, ebm_eng_ele in enumerate(self.vars_axis['ebmeng']) if ebm_eng_ele > self.ebeam_eng),0)-1
# self.hists_all['num_events'][0] += 1
if (self.ebm_ind != -1 and self.pls_ind != -1):
self.sortDataE(self.ebm_ind)
self.hists_all['ebm_pls_eng'][self.ebm_ind, self.pls_ind] += 1
if self.pho_ind != -1:
self.hists_all['ebm_pho_eng'][self.ebm_ind, self.pho_ind] += 1
self.hists_all['num_events'][0] += 1
# print self.mpi_rank, self.hists_all['num_events'][0]
if self.role == 'master' and (self.hists_all['num_events'][0]*self.mpi_size) % self.speed_rep_int == 0:
self.time = time.time()
print('Processed: {0} in {1:.2f} seconds ({2:.2f} Hz)'.format(
self.mpi_size*self.hists_all['num_events'][0],
self.time - self.old_time,
float(self.speed_rep_int)/float(self.time-self.old_time)))
sys.stdout.flush()
self.old_time = self.time
if self.role == 'master' and self.mpi_size*self.hists_all['num_events'][0] == self.output_params['max_events'] :
self.save_data()
self.shutdown(msg='maximum number of events reached.')
return
def sortDataE(self,ebm_ind):
e_peaks = self.alg.peak_finder_v4r2(self.eImage, thr_low=self.thr_low, thr_high=self.thr_high, rank=self.rank, r0=self.r0, dr=self.dr)
eXs = e_peaks[:,1].astype(int)
eYs = e_peaks[:,2].astype(int)
eRadius, eAngle =self.cart2polar(eXs, eYs)
for e_ind in range(len(eXs)):
tempXind = next((eX_ind for eX_ind, eX_ele in enumerate(self.vars_axis['ex']) if eX_ele > eXs[e_ind]),0)-1
tempYind = next((eY_ind for eY_ind, eY_ele in enumerate(self.vars_axis['ey']) if eY_ele > eYs[e_ind]),0)-1
if tempXind != -1 and tempYind != -1:
self.hists_all['ebm_ex_ey'][ebm_ind,tempXind, tempYind] += 1
def sortDataIon(self):
self.tof_inds = []
self.acqiris_data_wf[2:7] = self.acqiris_data_wf[2:7] - np.mean(self.acqiris_data_wf[2:7,self.acqiris_data_wt[6,:]>8000], axis=1)[:,np.newaxis]
#self.acqiris_data_wf[self.t_channel] = -self.acqiris_data_wf[self.t_channel]
t_peaks = np.array(self.PeakFinderT.cfd(self.acqiris_data_wf[self.t_channel],self.acqiris_data_wt[self.t_channel]))
if len(t_peaks) > 0:
# for t_peak in t_peaks:
# tempMCPind = next((MCP_ind for MCP_ind, MCP_ele in enumerate(self.vars_axis['t']) if MCP_ele > t_peak),0)-1
# if tempMCPind != -1:
# self.MCPTof[tempMCPind] += 1
# self.updateMCPGatesIon(tempMCPind)
x1_peaks = np.array(self.PeakFinderX.cfd(self.acqiris_data_wf[self.x1_channel],self.acqiris_data_wt[self.x1_channel]))
x2_peaks = np.array(self.PeakFinderX.cfd(self.acqiris_data_wf[self.x2_channel],self.acqiris_data_wt[self.x2_channel]))
y1_peaks = np.array(self.PeakFinderY.cfd(self.acqiris_data_wf[self.y1_channel],self.acqiris_data_wt[self.y1_channel]))
y2_peaks = np.array(self.PeakFinderY.cfd(self.acqiris_data_wf[self.y2_channel],self.acqiris_data_wt[self.y2_channel]))
# [(mcp_peak, (x, y,), (x1, x2, y1, y2)), ...]
ion_hits = self.HitFinder.FindHits(t_peaks, x1_peaks, x2_peaks, y1_peaks, y2_peaks)
for ion_hit in ion_hits:
tempTofind = next((Tof_ind for Tof_ind, Tof_ele in enumerate(self.vars_axis['t']) if Tof_ele > ion_hit[0]),0)-1
tempXind = next((X_ind for X_ind, X_ele in enumerate(self.vars_axis['x']) if X_ele > ion_hit[1]),0)-1
tempYind = next((Y_ind for Y_ind, Y_ele in enumerate(self.vars_axis['y']) if Y_ele > ion_hit[2]),0)-1
if tempTofind != -1 and tempXind != -1 and tempYind != -1:
self.tof_inds.append(tempTofind)
self.hists_all['t_'][tempTofind] += 1
self.hists_all['x_y'][tempXind, tempYind] += 1
# self.XT[tempTofind, tempXind] += 1
# self.YT[tempTofind, tempYind] += 1
self.updateTofGatesIon(tempTofind, tempXind, tempYind, ion_hit[0], ion_hit[1], ion_hit[2])
self.updatePiPiCoGatesIon(tempTofind, tempXind, tempYind, ion_hit[0], ion_hit[1], ion_hit[2])
self.tof_inds = sorted(self.tof_inds)
if len(self.tof_inds) > 1:
for i_tof_ind in range(len(self.tof_inds)-1):
# print '****************************************',self.PiPiCo.shape, len(self.tof_inds)
self.hists_all['pipico'][self.tof_inds[i_tof_ind],self.tof_inds[(i_tof_ind+1):]] += 1
if len(self.tof_inds) > 0:
F_sum,_ = np.histogram(self.HitFinder.F_sum, bins=self.TSum_axis)
S_sum,_ = np.histogram(self.HitFinder.S_sum, bins=self.TSum_axis)
self.hists_all['F_sum'] += F_sum
self.hists_all['S_sum'] += S_sum
def updateMCPGatesIon(self, t_ind):
for M_key, M_item in self.MCPGates.iteritems():
M_item.update_ion(t_ind)
def updateTofGatesIon(self, t_ind,x_ind,y_ind,tele,xele,yele):
for T_key, T_item in self.TofGates.iteritems():
T_item.update_ion(t_ind, x_ind, y_ind,tele,xele,yele)
def updatePiPiCoGatesIon(self, t_ind, xind, yind,tele,xele,yele):
for P_key, P_item in self.PiPiCoGates.iteritems():
P_item.update_ion(t_ind, xind, yind,tele,xele,yele)
def updateMCPGatesE(self, eAind, eRind, eXind, eYind):
for M_key, M_item in self.MCPGates.iteritems():
if M_item.is_coin():
M_item.update_electron(eAind, eRind, eXind, eYind)
def updateTofGatesE(self, eAind, eRind, eXind, eYind):
for T_key, T_item in self.TofGates.iteritems():
if T_item.is_coin():
T_item.update_electron(eAind, eRind, eXind, eYind)
def updatePiPiCoGatesE(self, eAind, eRind, eXind, eYind, pho_ind, pls_ind):
for P_key, P_item in self.PiPiCoGates.iteritems():
if P_item.is_coin():
P_item.update_electron(eAind, eRind, eXind, eYind, pho_ind, pls_ind)
def updateShotInfo(self,phoInd, plsInd,phoEng,plsEng):
for M_key, M_item in self.MCPGates.iteritems():
if M_item.is_coin():
M_item.update_shotinfo(phoInd, plsInd)
M_item.reset_coin_var()
for T_key, T_item in self.TofGates.iteritems():
if T_item.is_coin():
T_item.update_shotinfo(phoInd, plsInd)
T_item.reset_coin_var()
for P_key, P_item in self.PiPiCoGates.iteritems():
if P_item.is_coin():
P_item.update_shotinfo(phoInd, plsInd,phoEng,plsEng)
P_item.reset_coin_var()
def reduce(self):
print(str(self.mpi_rank)+' starts reduce.')
for histname in self.hist_names_all:
# self.time_a = time.time()
try:
self.time_a = time.time()
MPI.COMM_WORLD.Reduce(self.hists_all[histname],self.hists_all_all[histname])
self.time_b = time.time()
print(histname+' reduced by '+'Rank {0} in {1:.2f} seconds'.format(self.mpi_rank,self.time_b - self.time_a))
except Exception as e:
print(histname+' failed to be redueced by '+str(self.mpi_rank))
print(e)
# self.time_b = time.time()
# print(histname+' reduced by '+'Rank {0} in {1:.2f} seconds'.format(self.mpi_rank,self.time_b - self.time_a))
def save_data(self,num_lost_events_timecond,num_lost_events_datacond,num_lost_events_evtcond,num_failed_events,num_reduced_events):
print 'saving hdf5 file'
h5f = h5py.File(self.output_path,'w')
grp = h5f.create_group('all')
try:
grp.create_dataset('num_lost_events_timecond',data = num_lost_events_timecond)
grp.create_dataset('num_lost_events_datacond',data = num_lost_events_datacond)
grp.create_dataset('num_lost_events_evtcond',data = num_lost_events_evtcond)
grp.create_dataset('num_failed_events',data = num_failed_events)
grp.create_dataset('num_reduced_events',data = num_reduced_events)
except Exception as e:
print('events stats'+' failed to be saved.')
print(e)
for histname in self.hist_names_all:
try:
grp.create_dataset(histname,data = self.hists_all_all[histname])
except Exception as e:
print(histname+' failed to be saved.')
print(e)
grp1 = h5f.create_group('axis')
for var in self.vars:
try:
grp1.create_dataset(var+'_axis',data = self.vars_axis[var])
except Exception as e:
print(var+'_axis failed to be saved.')
print(e)
h5f.close()
print 'saved hdf5 file'
def init_gates(self):
self.PiPiCoGates = {}
for PPG in range(self.PiPiCoGates_params['num_gates']):
gateName = self.PiPiCoGates_params['gate'+str(PPG+1)+'_name']
tof1s = self.PiPiCoGates_params['gate'+str(PPG+1)+'_tof1s']
tof1l = self.PiPiCoGates_params['gate'+str(PPG+1)+'_tof1l']
tof2s = self.PiPiCoGates_params['gate'+str(PPG+1)+'_tof2s']
tof2l = self.PiPiCoGates_params['gate'+str(PPG+1)+'_tof2l']
tof3s = self.PiPiCoGates_params['gate'+str(PPG+1)+'_tof3s']
tof3l = self.PiPiCoGates_params['gate'+str(PPG+1)+'_tof3l']
thresh1_n3n1 = self.PiPiCoGates_params['gate'+str(PPG+1)+'_thresh1_n3n1']
thresh2_n3n1 = self.PiPiCoGates_params['gate'+str(PPG+1)+'_thresh2_n3n1']
thresh1_n3n2 = self.PiPiCoGates_params['gate'+str(PPG+1)+'_thresh1_n3n2']
thresh2_n3n2 = self.PiPiCoGates_params['gate'+str(PPG+1)+'_thresh2_n3n2']
self.PiPiCoGates[gateName] = PiPiCoGate(self, gateName, tof1s, tof1l, tof2s, tof2l,tof3s, tof3l,thresh1_n3n1,thresh2_n3n1,thresh1_n3n2,thresh2_n3n2,self.ang_f)
self.TofGates = {}
for TofG in range(self.TofGates_params['num_gates']):
gateName = self.TofGates_params['gate'+str(TofG+1)+'_name']
tofs = self.TofGates_params['gate'+str(TofG+1)+'_tofs']
tofl = self.TofGates_params['gate'+str(TofG+1)+'_tofl']
thresh1_tof = self.TofGates_params['gate'+str(TofG+1)+'_thresh1']
thresh2_tof = self.TofGates_params['gate'+str(TofG+1)+'_thresh2']
self.TofGates[gateName] = TofGate(self, gateName, tofs, tofl,thresh1_tof,thresh2_tof)
self.MCPGates = {}
for MCPG in range(self.MCPGates_params['num_gates']):
gateName = self.MCPGates_params['gate'+str(MCPG+1)+'_name']
tofs = self.MCPGates_params['gate'+str(MCPG+1)+'_tofs']
tofl = self.MCPGates_params['gate'+str(MCPG+1)+'_tofl']
self.MCPGates[gateName] = MCPGate(self, gateName, tofs, tofl)
def __init__(self,
exp,
run,
detname,
evt,
detector,
algorithm,
hitParam_alg_npix_min,
hitParam_alg_npix_max,
hitParam_alg_amax_thr,
hitParam_alg_atot_thr,
hitParam_alg_son_min,
streakMask_on,
streakMask_sigma,
streakMask_width,
userMask_path,
psanaMask_on,
psanaMask_calib,
psanaMask_status,
psanaMask_edges,
psanaMask_central,
psanaMask_unbond,
psanaMask_unbondnrs,
medianFilterOn=0,
medianRank=5,
radialFilterOn=0,
distance=0.0,
windows=None,
**kwargs):
self.exp = exp
self.run = run
self.detname = detname
self.det = detector
self.algorithm = algorithm
self.maxRes = 0
self.npix_min = hitParam_alg_npix_min
self.npix_max = hitParam_alg_npix_max
self.amax_thr = hitParam_alg_amax_thr
self.atot_thr = hitParam_alg_atot_thr
self.son_min = hitParam_alg_son_min
self.streakMask_on = str2bool(streakMask_on)
self.streakMask_sigma = streakMask_sigma
self.streakMask_width = streakMask_width
self.userMask_path = userMask_path
self.psanaMask_on = str2bool(psanaMask_on)
self.psanaMask_calib = str2bool(psanaMask_calib)
self.psanaMask_status = str2bool(psanaMask_status)
self.psanaMask_edges = str2bool(psanaMask_edges)
self.psanaMask_central = str2bool(psanaMask_central)
self.psanaMask_unbond = str2bool(psanaMask_unbond)
self.psanaMask_unbondnrs = str2bool(psanaMask_unbondnrs)
self.medianFilterOn = medianFilterOn
self.medianRank = medianRank
self.radialFilterOn = radialFilterOn
self.distance = distance
self.windows = windows
self.userMask = None
self.psanaMask = None
self.streakMask = None
self.userPsanaMask = None
self.combinedMask = None
# Make user mask
if self.userMask_path is not None:
self.userMask = np.load(self.userMask_path)
# Make psana mask
if self.psanaMask_on:
self.psanaMask = detector.mask(evt,
calib=self.psanaMask_calib,
status=self.psanaMask_status,
edges=self.psanaMask_edges,
central=self.psanaMask_central,
unbond=self.psanaMask_unbond,
unbondnbrs=self.psanaMask_unbondnrs)
# Combine userMask and psanaMask
self.userPsanaMask = np.ones_like(self.det.calib(evt))
if self.userMask is not None:
self.userPsanaMask *= self.userMask
if self.psanaMask is not None:
self.userPsanaMask *= self.psanaMask
# Powder of hits and misses
self.powderHits = np.zeros_like(self.userPsanaMask)
self.powderMisses = np.zeros_like(self.userPsanaMask)
self.alg = PyAlgos(windows=self.windows,
mask=self.userPsanaMask,
pbits=0)
# set peak-selector parameters:
self.alg.set_peak_selection_pars(npix_min=self.npix_min, npix_max=self.npix_max, \
amax_thr=self.amax_thr, atot_thr=self.atot_thr, \
son_min=self.son_min)
# set algorithm specific parameters
if algorithm == 1:
self.hitParam_alg1_thr_low = kwargs["alg1_thr_low"]
self.hitParam_alg1_thr_high = kwargs["alg1_thr_high"]
self.hitParam_alg1_rank = int(kwargs["alg1_rank"])
self.hitParam_alg1_radius = int(kwargs["alg1_radius"])
self.hitParam_alg1_dr = kwargs["alg1_dr"]
elif algorithm == 3:
self.hitParam_alg3_rank = kwargs["alg3_rank"]
self.hitParam_alg3_r0 = int(kwargs["alg3_r0"])
self.hitParam_alg3_dr = kwargs["alg3_dr"]
elif algorithm == 4:
self.hitParam_alg4_thr_low = kwargs["alg4_thr_low"]
self.hitParam_alg4_thr_high = kwargs["alg4_thr_high"]
self.hitParam_alg4_rank = int(kwargs["alg4_rank"])
self.hitParam_alg4_r0 = int(kwargs["alg4_r0"])
self.hitParam_alg4_dr = kwargs["alg4_dr"]
self.maxNumPeaks = 2048
self.StreakMask = myskbeam.StreakMask(self.det,
evt,
width=self.streakMask_width,
sigma=self.streakMask_sigma)
self.cx, self.cy = self.det.point_indexes(evt, pxy_um=(0, 0))
self.iX = np.array(self.det.indexes_x(evt), dtype=np.int64)
self.iY = np.array(self.det.indexes_y(evt), dtype=np.int64)
if len(self.iX.shape) == 2:
self.iX = np.expand_dims(self.iX, axis=0)
self.iY = np.expand_dims(self.iY, axis=0)
# Initialize radial background subtraction
self.setupExperiment()
if self.radialFilterOn:
self.setupRadialBackground()
self.updatePolarizationFactor()
from psana import *
import numpy as np
from ImgAlgos.PyAlgos import PyAlgos
from skbeam.core.accumulators.histogram import Histogram
dsource = MPIDataSource('exp=sxr07416:run=28:smd')
det = Detector('OPAL1')
alg = PyAlgos()
alg.set_peak_selection_pars(npix_min=9,
npix_max=100,
amax_thr=40,
atot_thr=300,
son_min=0)
hist_row = Histogram((1024, 0., 1024.))
hist_col = Histogram((1024, 0., 1024.))
hist_amp = Histogram((1024, 0., 3000.))
smldata = dsource.small_data('run28.h5', gather_interval=100)
peakrow = np.zeros((10), dtype=int)
peakcol = np.zeros((10), dtype=int)
peakamp = np.zeros((10), dtype=float)
for nevt, evt in enumerate(dsource.events()):
calib = det.calib(evt)
if calib is None: continue
peaks = alg.peak_finder_v1(calib,
thr_low=40,
thr_high=40,