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 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 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 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)
