class ImageViewer(object):
def __init__(self, parent = None):
self.parent = parent
self.maxPercentile = 0
self.minPercentile = 0
self.displayMaxPercentile = 99.0
self.displayMinPercentile = 1.0
self.rb = None
## Dock 1: Image Panel
self.d1 = Dock("Image Panel", size=(500, 400))
self.w1 = pg.ImageView(view=pg.PlotItem())
self.w1.getView().invertY(False)
self.img_feature = pg.ImageItem()
self.w1.getView().addItem(self.img_feature)
self.ring_feature = pg.ScatterPlotItem()
self.centre_feature = pg.ScatterPlotItem()
self.peak_feature = pg.ScatterPlotItem()
self.indexedPeak_feature = pg.ScatterPlotItem()
self.z_direction = pg.ScatterPlotItem()
self.z_direction1 = pg.ScatterPlotItem()
self.w1.getView().addItem(self.ring_feature)
self.w1.getView().addItem(self.centre_feature)
self.w1.getView().addItem(self.peak_feature)
self.w1.getView().addItem(self.indexedPeak_feature)
self.w1.getView().addItem(self.z_direction)
self.w1.getView().addItem(self.z_direction1)
self.abc_text = pg.TextItem(html='', anchor=(0,0)) # unit cell display
self.w1.getView().addItem(self.abc_text)
self.peak_text = pg.TextItem(html='', anchor=(0,0)) # peak display
self.w1.getView().addItem(self.peak_text)
# # Isocurve drawing
# self.iso = pg.IsocurveItem(level=0.8, pen='r')
# self.iso.setParentItem(self.img_feature)
# self.iso.setZValue(2)
# # Contrast/color control
# self.hist = pg.HistogramLUTItem()
# self.hist.setImageItem(self.img_feature)
# self.w1.getView().addItem(self.hist)
# # Draggable line for setting isocurve level
# self.isoLine = pg.InfiniteLine(angle=0, movable=True, pen='g')
# self.hist.vb.addItem(self.isoLine)
# self.hist.vb.setMouseEnabled(y=False) # makes user interaction a little easier
# self.isoLine.setValue(1.8)
# self.isoLine.setZValue(1000) # bring iso line above contrast controls
self.d1.addWidget(self.w1)
self.drawLabCoordinates() # FIXME: This does not match the lab coordinates yet!
def clearPeakMessage(self):
self.w1.getView().removeItem(self.peak_text)
self.peak_feature.setData([], [], pxMode=False)
if self.parent.args.v >= 1: print "Done clearPeakMessage"
def drawLabCoordinates(self):
(cenX,cenY) = (-20,-20) # no offset
# Draw xy arrows
symbolSize = 40
cutoff=symbolSize/2
headLen=30
tailLen=30-cutoff
xArrow = pg.ArrowItem(angle=180, tipAngle=30, baseAngle=20, headLen=headLen, tailLen=tailLen, tailWidth=8, pen=None, brush='b', pxMode=False)
xArrow.setPos(2*headLen+cenX, 0+cenY)
self.w1.getView().addItem(xArrow)
yArrow = pg.ArrowItem(angle=-90, tipAngle=30, baseAngle=20, headLen=headLen, tailLen=tailLen, tailWidth=8, pen=None, brush='r', pxMode=False)
yArrow.setPos(0+cenX, 2*headLen+cenY)
self.w1.getView().addItem(yArrow)
# Lab coordinates: Add z-direction
self.z_direction.setData([0+cenX], [0+cenY], symbol='o', \
size=symbolSize, brush='w', \
pen={'color': 'k', 'width': 4}, pxMode=False)
self.z_direction1.setData([0+cenX], [0+cenY], symbol='o', \
size=symbolSize/6, brush='k', \
pen={'color': 'k', 'width': 4}, pxMode=False)
# Lab coordinates: Add xyz text
self.x_text = pg.TextItem(html='<div style="text-align: center"><span style="color: #0000FF; font-size: 16pt;">x</span></div>', anchor=(0,0))
self.w1.getView().addItem(self.x_text)
self.x_text.setPos(2*headLen+cenX, 0+cenY)
self.y_text = pg.TextItem(html='<div style="text-align: center"><span style="color: #FF0000; font-size: 16pt;">y</span></div>', anchor=(1,1))
self.w1.getView().addItem(self.y_text)
self.y_text.setPos(0+cenX, 2*headLen+cenY)
self.z_text = pg.TextItem(html='<div style="text-align: center"><span style="color: #FFFFFF; font-size: 16pt;">z</span></div>', anchor=(1,0))
self.w1.getView().addItem(self.z_text)
self.z_text.setPos(-headLen+cenX, 0+cenY)
# Label xy axes
self.x_axis = self.w1.getView().getAxis('bottom')
self.x_axis.setLabel('X-axis (pixels)')
self.y_axis = self.w1.getView().getAxis('left')
self.y_axis.setLabel('Y-axis (pixels)')
def updateImage(self,calib=None):
if self.parent.hasExperimentName and self.parent.hasRunNumber and self.parent.hasDetInfo:
if calib is None:
self.parent.calib, self.parent.data = self.getDetImage(self.parent.eventNumber)
else:
_, self.parent.data = self.getDetImage(self.parent.eventNumber, calib=calib)
if self.parent.firstUpdate:
if self.parent.exp.logscaleOn:
self.w1.setImage(np.log10(abs(self.parent.data) + self.parent.eps))
self.parent.firstUpdate = False
else:
self.minPercentile = np.percentile(self.parent.data, self.displayMinPercentile)
if self.minPercentile < 0: self.minPercentile = 0
self.maxPercentile = np.percentile(self.parent.data, self.displayMaxPercentile)
self.w1.setImage(self.parent.data, levels=(self.minPercentile, self.maxPercentile))
self.parent.firstUpdate = False
else:
if self.parent.exp.logscaleOn:
self.w1.setImage(np.log10(abs(self.parent.data) + self.parent.eps),
autoRange=False, autoLevels=False, autoHistogramRange=False)
else:
if self.minPercentile == 0 and self.maxPercentile == 0:
self.minPercentile = np.percentile(self.parent.data, self.displayMinPercentile)
if self.minPercentile < 0: self.minPercentile = 0
self.maxPercentile = np.percentile(self.parent.data, self.displayMaxPercentile)
self.w1.setImage(self.parent.data, levels=(self.minPercentile, self.maxPercentile))
else:
self.w1.setImage(self.parent.data, autoRange=False, autoLevels=False, autoHistogramRange=False)
try:
fname = "/reg/d/psdm/cxi/cxitut13/res/psocake/log/" + \
self.parent.exp.username + "_" + self.parent.experimentName + "_" \
+ str(self.parent.runNumber) + "_" + self.parent.detInfo + ".txt"
with open(fname, "a") as myfile:
date = datetime.datetime.today().strftime('%Y-%m-%d-%H-%M-%S')
myStr = date + ": " + str(self.parent.eventNumber) + '\n'
myfile.write(myStr)
except:
pass
# Load peak parameters if exists
if 'sfx' in self.parent.args.mode and self.parent.pk.userUpdate is None:
self.parent.pk.updateParam()
if self.parent.args.v >= 1: print "Done updateImage"
def getCalib(self, evtNumber):
if self.parent.exp.run is not None:
self.parent.evt = self.parent.exp.getEvt(evtNumber)
if self.parent.exp.applyCommonMode: # play with different common mode
if self.parent.exp.commonMode[0] == 5: # Algorithm 5
calib = self.parent.det.calib(self.parent.evt,
cmpars=(self.parent.exp.commonMode[0], self.parent.exp.commonMode[1]))
else: # Algorithms 1 to 4
print "### Overriding common mode: ", self.parent.exp.commonMode
calib = self.parent.det.calib(self.parent.evt,
cmpars=(self.parent.exp.commonMode[0], self.parent.exp.commonMode[1],
self.parent.exp.commonMode[2], self.parent.exp.commonMode[3]))
else:
calib = self.parent.det.calib(self.parent.evt)
return calib
else:
return None
def getCommonModeCorrected(self, evtNumber):
if self.parent.exp.run is not None:
try:
self.parent.evt = self.parent.exp.getEvt(evtNumber)
pedestalCorrected = self.parent.det.raw(self.parent.evt) - self.parent.det.pedestals(self.parent.evt)
if self.parent.exp.applyCommonMode: # play with different common mode
if self.parent.exp.commonMode[0] == 5: # Algorithm 5
commonMode = self.parent.det.common_mode_correction(self.parent.evt, pedestalCorrected,
cmpars=(self.parent.exp.commonMode[0],
self.parent.exp.commonMode[1]))
commonModeCorrected = pedestalCorrected - commonMode
else: # Algorithms 1 to 4
print "### Overriding common mode: ", self.parent.exp.commonMode
commonMode = self.parent.det.common_mode_correction(self.parent.evt, pedestalCorrected,
cmpars=(self.parent.exp.commonMode[0], self.parent.exp.commonMode[1],
self.parent.exp.commonMode[2], self.parent.exp.commonMode[3]))
commonModeCorrected = pedestalCorrected - commonMode
else:
commonMode = self.parent.det.common_mode_correction(self.parent.evt, pedestalCorrected)
commonModeCorrected = pedestalCorrected + commonMode # WHAT! You need to ADD common mode?!!
return commonModeCorrected
except:
return None
else:
return None
def getCommonMode(self, evtNumber):
if self.parent.exp.run is not None:
self.parent.evt = self.parent.exp.getEvt(evtNumber)
pedestalCorrected = self.parent.det.raw(self.parent.evt) - self.parent.det.pedestals(self.parent.evt)
if self.parent.exp.applyCommonMode: # play with different common mode
print "### Overriding common mode: ", self.parent.exp.commonMode
if self.parent.exp.commonMode[0] == 5: # Algorithm 5
cm = self.parent.det.common_mode_correction(self.parent.evt, pedestalCorrected,
cmpars=(self.parent.exp.commonMode[0],
self.parent.exp.commonMode[1]))
else: # Algorithms 1 to 4
cm = self.parent.det.common_mode_correction(self.parent.evt, pedestalCorrected,
cmpars=(self.parent.exp.commonMode[0],
self.parent.exp.commonMode[1],
self.parent.exp.commonMode[2],
self.parent.exp.commonMode[3]))
else:
cm = self.parent.det.common_mode_correction(self.parent.evt, pedestalCorrected)
return cm
else:
return None
def getAssembledImage(self, arg, calib):
if arg == 'lcls':
_calib = calib.copy() # this is important
tic = time.time()
if self.parent.exp.applyFriedel: # Apply Friedel symmetry
print "Apply Friedel symmetry"
centre = self.parent.det.point_indexes(self.parent.evt, pxy_um=(0, 0))
self.fs = FriedelSym(self.parent.exp.detGuaranteedData.shape, centre)
data = self.parent.det.image(self.parent.evt, _calib)
if self.parent.mk.combinedMask is not None:
data = self.fs.applyFriedel(data, mask=self.parent.det.image(self.parent.evt, self.parent.mk.combinedMask), mode='same')
else:
data = self.fs.applyFriedel(data, mask=None, mode='same')
else:
data = self.parent.det.image(self.parent.evt, _calib)
if data is None: data = _calib
toc = time.time()
if self.parent.args.v >= 1: print "time assemble: ", toc-tic
return data
def setupRadialBackground(self):
self.parent.geom.findPsanaGeometry()
if self.parent.geom.calibFile is not None:
if self.parent.args.v >= 1: print "calibFile: ", self.parent.geom.calibPath+'/'+self.parent.geom.calibFile
self.geo = self.parent.det.geometry(self.parent.runNumber) #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.iY = self.geo.get_pixel_coord_indexes()
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)
if self.parent.args.v >= 1: print "Done setupRadialBackground"
else:
self.rb = None
def updatePolarizationFactor(self):
if self.rb is not None:
self.pf = polarization_factor(self.rb.pixel_rad(), self.rb.pixel_phi(), self.parent.detectorDistance*1e6) # convert to um
if self.parent.args.v >= 1: print "Done updatePolarizationFactor"
def updateDetectorCentre(self, arg):
if arg == 'lcls':
self.parent.cx, self.parent.cy = self.parent.det.point_indexes(self.parent.evt, pxy_um=(0, 0))
if self.parent.cx is None:
data = self.parent.det.image(self.parent.evt, self.parent.exp.detGuaranteed)
self.parent.cx, self.parent.cy = self.getCentre(data.shape)
if self.parent.args.v >= 1: print "cx, cy: ", self.parent.cx, self.parent.cy
def getDetImage(self, evtNumber, calib=None):
if calib is None:
if self.parent.exp.image_property == self.parent.exp.disp_medianCorrection: # median subtraction
calib = self.getCalib(evtNumber)
if calib is None: calib = np.zeros_like(self.parent.exp.detGuaranteed, dtype='float32')
calib -= median_filter_ndarr(calib, self.parent.exp.medianFilterRank)
elif self.parent.exp.image_property == self.parent.exp.disp_radialCorrection: # radial subtraction + polarization corrected
calib = self.getCalib(evtNumber)
if calib is None: calib = np.zeros_like(self.parent.exp.detGuaranteed, dtype='float32')
self.pf.shape = calib.shape # FIXME: shape is 1d
calib = self.rb.subtract_bkgd(calib * self.pf)
calib.shape = self.parent.calib.shape # FIXME: shape is 1d
elif self.parent.exp.image_property == self.parent.exp.disp_adu: # gain and hybrid gain corrected
calib = self.getCalib(evtNumber)
if calib is None: calib = np.zeros_like(self.parent.exp.detGuaranteed, dtype='float32')
elif self.parent.exp.image_property == self.parent.exp.disp_commonModeCorrected: # common mode corrected
calib = self.getCommonModeCorrected(evtNumber)
if calib is None: calib = np.zeros_like(self.parent.exp.detGuaranteed, dtype='float32')
elif self.parent.exp.image_property == self.parent.exp.disp_pedestalCorrected: # pedestal corrected
calib = self.parent.det.raw(self.parent.evt).astype('float32')
if calib is None:
calib = np.zeros_like(self.parent.exp.detGuaranteed, dtype='float32')
else:
calib -= self.parent.det.pedestals(self.parent.evt)
elif self.parent.exp.image_property == self.parent.exp.disp_raw: # raw
calib = self.parent.det.raw(self.parent.evt)
if calib is None:
calib = np.zeros_like(self.parent.exp.detGuaranteed, dtype='float32')
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_photons: # photon counts
calib = self.parent.det.photons(self.parent.evt, mask=self.parent.mk.userMask, adu_per_photon=self.parent.exp.aduPerPhoton)
if calib is None:
calib = np.zeros_like(self.parent.exp.detGuaranteed, dtype='int32')
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_pedestal: # pedestal
calib = self.parent.det.pedestals(self.parent.evt)
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_status: # status
calib = self.parent.det.status(self.parent.evt)
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_rms: # rms
calib = self.parent.det.rms(self.parent.evt)
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_commonMode: # common mode
calib = self.getCommonMode(evtNumber)
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_gain: # gain
calib = self.parent.det.gain(self.parent.evt)
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_gainMask: # gain_mask
calib = self.parent.det.gain_mask(self.parent.evt)
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_coordx: # coords_x
calib = self.parent.det.coords_x(self.parent.evt)
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_coordy: # coords_y
calib = self.parent.det.coords_y(self.parent.evt)
self.parent.firstUpdate = True
shape = self.parent.det.shape(self.parent.evt)
if len(shape) == 3:
if self.parent.exp.image_property == self.parent.exp.disp_quad: # quad ind
calib = np.zeros(shape)
for i in range(shape[0]):
# TODO: handle detectors properly
if shape[0] == 32: # cspad
calib[i,:,:] = int(i)%8
elif shape[0] == 2: # cspad2x2
calib[i,:,:] = int(i)%2
elif shape[0] == 4: # pnccd
calib[i,:,:] = int(i)%4
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_seg: # seg ind
calib = np.zeros(shape)
if shape[0] == 32: # cspad
for i in range(32):
calib[i,:,:] = int(i)/8
elif shape[0] == 2: # cspad2x2
for i in range(2):
calib[i,:,:] = int(i)
elif shape[0] == 4: # pnccd
for i in range(4):
calib[i,:,:] = int(i)
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_row: # row ind
calib = np.zeros(shape)
if shape[0] == 32: # cspad
for i in range(185):
calib[:,i,:] = i
elif shape[0] == 2: # cspad2x2
for i in range(185):
calib[:,i,:] = i
elif shape[0] == 4: # pnccd
for i in range(512):
calib[:,i,:] = i
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_col: # col ind
calib = np.zeros(shape)
if shape[0] == 32: # cspad
for i in range(388):
calib[:,:,i] = i
elif shape[0] == 2: # cspad2x2
for i in range(388):
calib[:,:,i] = i
elif shape[0] == 4: # pnccd
for i in range(512):
calib[:,:,i] = i
self.parent.firstUpdate = True
# Update photon energy
self.parent.exp.updatePhotonEnergy('lcls')
# Update clen
self.parent.geom.updateClen('lcls')
# Write a temporary geom file
self.parent.geom.deployCrystfelGeometry('lcls')
self.parent.geom.writeCrystfelGeom('lcls') # Hack to override coffset
# Get assembled image
if calib is not None:
data = self.getAssembledImage('lcls', calib)
else:
calib = np.zeros_like(self.parent.exp.detGuaranteed, dtype='float32')
data = self.getAssembledImage('lcls', calib)
# Update detector centre
self.updateDetectorCentre('lcls')
# Update ROI histogram
if self.parent.roi.roiCurrent == 'rect':
self.parent.roi.updateRoi(self.parent.roi.roi)
elif self.parent.roi.roiCurrent == 'poly':
self.parent.roi.updateRoi(self.parent.roi.roiPoly)
elif self.parent.roi.roiCurrent == 'circ':
self.parent.roi.updateRoi(self.parent.roi.roiCircle)
return calib, data
def getCentre(self,shape):
cx = shape[1]/2
cy = shape[0]/2
return cx,cy
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 ImageViewer(object):
def __init__(self, parent=None):
self.parent = parent
self.maxPercentile = 0
self.minPercentile = 0
self.displayMaxPercentile = 99.0
self.displayMinPercentile = 1.0
self.rb = None
## Dock 1: Image Panel
self.d1 = Dock("Image Panel", size=(500, 400))
self.w1 = pg.ImageView(view=pg.PlotItem())
self.w1.getView().invertY(False)
self.img_feature = pg.ImageItem()
self.w1.getView().addItem(self.img_feature)
self.ring_feature = pg.ScatterPlotItem()
self.centre_feature = pg.ScatterPlotItem()
self.peak_feature = pg.ScatterPlotItem()
self.indexedPeak_feature = pg.ScatterPlotItem()
self.z_direction = pg.ScatterPlotItem()
self.z_direction1 = pg.ScatterPlotItem()
self.w1.getView().addItem(self.ring_feature)
self.w1.getView().addItem(self.centre_feature)
self.w1.getView().addItem(self.peak_feature)
self.w1.getView().addItem(self.indexedPeak_feature)
self.w1.getView().addItem(self.z_direction)
self.w1.getView().addItem(self.z_direction1)
self.abc_text = pg.TextItem(html='',
anchor=(0, 0)) # unit cell display
self.w1.getView().addItem(self.abc_text)
self.peak_text = pg.TextItem(html='', anchor=(0, 0)) # peak display
self.w1.getView().addItem(self.peak_text)
# # Isocurve drawing
# self.iso = pg.IsocurveItem(level=0.8, pen='r')
# self.iso.setParentItem(self.img_feature)
# self.iso.setZValue(2)
# # Contrast/color control
# self.hist = pg.HistogramLUTItem()
# self.hist.setImageItem(self.img_feature)
# self.w1.getView().addItem(self.hist)
# # Draggable line for setting isocurve level
# self.isoLine = pg.InfiniteLine(angle=0, movable=True, pen='g')
# self.hist.vb.addItem(self.isoLine)
# self.hist.vb.setMouseEnabled(y=False) # makes user interaction a little easier
# self.isoLine.setValue(1.8)
# self.isoLine.setZValue(1000) # bring iso line above contrast controls
self.d1.addWidget(self.w1)
self.drawLabCoordinates(
) # FIXME: This does not match the lab coordinates yet!
def clearPeakMessage(self):
self.w1.getView().removeItem(self.peak_text)
self.peak_feature.setData([], [], pxMode=False)
if self.parent.args.v >= 1: print "Done clearPeakMessage"
def drawLabCoordinates(self):
(cenX, cenY) = (-20, -20) # no offset
# Draw xy arrows
symbolSize = 40
cutoff = symbolSize / 2
headLen = 30
tailLen = 30 - cutoff
xArrow = pg.ArrowItem(angle=180,
tipAngle=30,
baseAngle=20,
headLen=headLen,
tailLen=tailLen,
tailWidth=8,
pen=None,
brush='b',
pxMode=False)
xArrow.setPos(2 * headLen + cenX, 0 + cenY)
self.w1.getView().addItem(xArrow)
yArrow = pg.ArrowItem(angle=-90,
tipAngle=30,
baseAngle=20,
headLen=headLen,
tailLen=tailLen,
tailWidth=8,
pen=None,
brush='r',
pxMode=False)
yArrow.setPos(0 + cenX, 2 * headLen + cenY)
self.w1.getView().addItem(yArrow)
# Lab coordinates: Add z-direction
self.z_direction.setData([0+cenX], [0+cenY], symbol='o', \
size=symbolSize, brush='w', \
pen={'color': 'k', 'width': 4}, pxMode=False)
self.z_direction1.setData([0+cenX], [0+cenY], symbol='o', \
size=symbolSize/6, brush='k', \
pen={'color': 'k', 'width': 4}, pxMode=False)
# Lab coordinates: Add xyz text
self.x_text = pg.TextItem(
html=
'<div style="text-align: center"><span style="color: #0000FF; font-size: 16pt;">x</span></div>',
anchor=(0, 0))
self.w1.getView().addItem(self.x_text)
self.x_text.setPos(2 * headLen + cenX, 0 + cenY)
self.y_text = pg.TextItem(
html=
'<div style="text-align: center"><span style="color: #FF0000; font-size: 16pt;">y</span></div>',
anchor=(1, 1))
self.w1.getView().addItem(self.y_text)
self.y_text.setPos(0 + cenX, 2 * headLen + cenY)
self.z_text = pg.TextItem(
html=
'<div style="text-align: center"><span style="color: #FFFFFF; font-size: 16pt;">z</span></div>',
anchor=(1, 0))
self.w1.getView().addItem(self.z_text)
self.z_text.setPos(-headLen + cenX, 0 + cenY)
# Label xy axes
self.x_axis = self.w1.getView().getAxis('bottom')
self.x_axis.setLabel('X-axis (pixels)')
self.y_axis = self.w1.getView().getAxis('left')
self.y_axis.setLabel('Y-axis (pixels)')
def updateImage(self, calib=None):
if self.parent.hasExperimentName and self.parent.hasRunNumber and self.parent.hasDetInfo:
if calib is None:
self.parent.calib, self.parent.data = self.getDetImage(
self.parent.eventNumber)
else:
_, self.parent.data = self.getDetImage(self.parent.eventNumber,
calib=calib)
if self.parent.firstUpdate:
if self.parent.exp.logscaleOn:
self.w1.setImage(
np.log10(abs(self.parent.data) + self.parent.eps))
self.parent.firstUpdate = False
else:
self.minPercentile = np.percentile(
self.parent.data, self.displayMinPercentile)
if self.minPercentile < 0: self.minPercentile = 0
self.maxPercentile = np.percentile(
self.parent.data, self.displayMaxPercentile)
self.w1.setImage(self.parent.data,
levels=(self.minPercentile,
self.maxPercentile))
self.parent.firstUpdate = False
else:
if self.parent.exp.logscaleOn:
self.w1.setImage(
np.log10(abs(self.parent.data) + self.parent.eps),
autoRange=False,
autoLevels=False,
autoHistogramRange=False)
else:
if self.minPercentile == 0 and self.maxPercentile == 0:
self.minPercentile = np.percentile(
self.parent.data, self.displayMinPercentile)
if self.minPercentile < 0: self.minPercentile = 0
self.maxPercentile = np.percentile(
self.parent.data, self.displayMaxPercentile)
self.w1.setImage(self.parent.data,
levels=(self.minPercentile,
self.maxPercentile))
else:
self.w1.setImage(self.parent.data,
autoRange=False,
autoLevels=False,
autoHistogramRange=False)
try:
fname = "/reg/d/psdm/cxi/cxitut13/res/psocake/log/" + \
self.parent.exp.username + "_" + self.parent.experimentName + "_" \
+ str(self.parent.runNumber) + "_" + self.parent.detInfo + ".txt"
with open(fname, "a") as myfile:
date = datetime.datetime.today().strftime(
'%Y-%m-%d-%H-%M-%S')
myStr = date + ": " + str(self.parent.eventNumber) + '\n'
myfile.write(myStr)
except:
pass
# Load peak parameters if exists
if 'sfx' in self.parent.args.mode and self.parent.pk.userUpdate is None:
self.parent.pk.updateParam()
if self.parent.args.v >= 1: print "Done updateImage"
def getCalib(self, evtNumber):
if self.parent.exp.run is not None:
self.parent.evt = self.parent.exp.getEvt(evtNumber)
if self.parent.exp.applyCommonMode: # play with different common mode
if self.parent.exp.commonMode[0] == 5: # Algorithm 5
calib = self.parent.det.calib(
self.parent.evt,
cmpars=(self.parent.exp.commonMode[0],
self.parent.exp.commonMode[1]))
else: # Algorithms 1 to 4
print "### Overriding common mode: ", self.parent.exp.commonMode
calib = self.parent.det.calib(
self.parent.evt,
cmpars=(self.parent.exp.commonMode[0],
self.parent.exp.commonMode[1],
self.parent.exp.commonMode[2],
self.parent.exp.commonMode[3]))
else:
calib = self.parent.det.calib(self.parent.evt)
return calib
else:
return None
def getCommonModeCorrected(self, evtNumber):
if self.parent.exp.run is not None:
try:
self.parent.evt = self.parent.exp.getEvt(evtNumber)
pedestalCorrected = self.parent.det.raw(
self.parent.evt) - self.parent.det.pedestals(
self.parent.evt)
if self.parent.exp.applyCommonMode: # play with different common mode
if self.parent.exp.commonMode[0] == 5: # Algorithm 5
commonMode = self.parent.det.common_mode_correction(
self.parent.evt,
pedestalCorrected,
cmpars=(self.parent.exp.commonMode[0],
self.parent.exp.commonMode[1]))
commonModeCorrected = pedestalCorrected - commonMode
else: # Algorithms 1 to 4
print "### Overriding common mode: ", self.parent.exp.commonMode
commonMode = self.parent.det.common_mode_correction(
self.parent.evt,
pedestalCorrected,
cmpars=(self.parent.exp.commonMode[0],
self.parent.exp.commonMode[1],
self.parent.exp.commonMode[2],
self.parent.exp.commonMode[3]))
commonModeCorrected = pedestalCorrected - commonMode
else:
commonMode = self.parent.det.common_mode_correction(
self.parent.evt, pedestalCorrected)
commonModeCorrected = pedestalCorrected + commonMode # WHAT! You need to ADD common mode?!!
return commonModeCorrected
except:
return None
else:
return None
def getCommonMode(self, evtNumber):
if self.parent.exp.run is not None:
self.parent.evt = self.parent.exp.getEvt(evtNumber)
pedestalCorrected = self.parent.det.raw(
self.parent.evt) - self.parent.det.pedestals(self.parent.evt)
if self.parent.exp.applyCommonMode: # play with different common mode
print "### Overriding common mode: ", self.parent.exp.commonMode
if self.parent.exp.commonMode[0] == 5: # Algorithm 5
cm = self.parent.det.common_mode_correction(
self.parent.evt,
pedestalCorrected,
cmpars=(self.parent.exp.commonMode[0],
self.parent.exp.commonMode[1]))
else: # Algorithms 1 to 4
cm = self.parent.det.common_mode_correction(
self.parent.evt,
pedestalCorrected,
cmpars=(self.parent.exp.commonMode[0],
self.parent.exp.commonMode[1],
self.parent.exp.commonMode[2],
self.parent.exp.commonMode[3]))
else:
cm = self.parent.det.common_mode_correction(
self.parent.evt, pedestalCorrected)
return cm
else:
return None
def getAssembledImage(self, arg, calib):
if arg == 'lcls':
_calib = calib.copy() # this is important
tic = time.time()
if self.parent.exp.applyFriedel: # Apply Friedel symmetry
print "Apply Friedel symmetry"
centre = self.parent.det.point_indexes(self.parent.evt,
pxy_um=(0, 0))
self.fs = FriedelSym(self.parent.exp.detGuaranteedData.shape,
centre)
data = self.parent.det.image(self.parent.evt, _calib)
if self.parent.mk.combinedMask is not None:
data = self.fs.applyFriedel(
data,
mask=self.parent.det.image(
self.parent.evt, self.parent.mk.combinedMask),
mode='same')
else:
data = self.fs.applyFriedel(data, mask=None, mode='same')
else:
data = self.parent.det.image(self.parent.evt, _calib)
if data is None: data = _calib
toc = time.time()
if self.parent.args.v >= 1: print "time assemble: ", toc - tic
return data
def setupRadialBackground(self):
self.parent.geom.findPsanaGeometry()
if self.parent.geom.calibFile is not None:
if self.parent.args.v >= 1:
print "calibFile: ", self.parent.geom.calibPath + '/' + self.parent.geom.calibFile
self.geo = self.parent.det.geometry(
self.parent.runNumber
) #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.iY = self.geo.get_pixel_coord_indexes()
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)
if self.parent.args.v >= 1: print "Done setupRadialBackground"
else:
self.rb = None
def updatePolarizationFactor(self):
if self.rb is not None:
self.pf = polarization_factor(self.rb.pixel_rad(),
self.rb.pixel_phi(),
self.parent.detectorDistance *
1e6) # convert to um
if self.parent.args.v >= 1: print "Done updatePolarizationFactor"
def updateDetectorCentre(self, arg):
if arg == 'lcls':
self.parent.cx, self.parent.cy = self.parent.det.point_indexes(
self.parent.evt, pxy_um=(0, 0))
if self.parent.cx is None:
data = self.parent.det.image(self.parent.evt,
self.parent.exp.detGuaranteed)
self.parent.cx, self.parent.cy = self.getCentre(data.shape)
if self.parent.args.v >= 1:
print "cx, cy: ", self.parent.cx, self.parent.cy
def getDetImage(self, evtNumber, calib=None):
if calib is None:
if self.parent.exp.image_property == self.parent.exp.disp_medianCorrection: # median subtraction
calib = self.getCalib(evtNumber)
if calib is None:
calib = np.zeros_like(self.parent.exp.detGuaranteed,
dtype='float32')
calib -= median_filter_ndarr(calib,
self.parent.exp.medianFilterRank)
elif self.parent.exp.image_property == self.parent.exp.disp_radialCorrection: # radial subtraction + polarization corrected
calib = self.getCalib(evtNumber)
if calib is None:
calib = np.zeros_like(self.parent.exp.detGuaranteed,
dtype='float32')
self.pf.shape = calib.shape # FIXME: shape is 1d
calib = self.rb.subtract_bkgd(calib * self.pf)
calib.shape = self.parent.calib.shape # FIXME: shape is 1d
elif self.parent.exp.image_property == self.parent.exp.disp_adu: # gain and hybrid gain corrected
calib = self.getCalib(evtNumber)
if calib is None:
calib = np.zeros_like(self.parent.exp.detGuaranteed,
dtype='float32')
elif self.parent.exp.image_property == self.parent.exp.disp_commonModeCorrected: # common mode corrected
calib = self.getCommonModeCorrected(evtNumber)
if calib is None:
calib = np.zeros_like(self.parent.exp.detGuaranteed,
dtype='float32')
elif self.parent.exp.image_property == self.parent.exp.disp_pedestalCorrected: # pedestal corrected
calib = self.parent.det.raw(self.parent.evt).astype('float32')
if calib is None:
calib = np.zeros_like(self.parent.exp.detGuaranteed,
dtype='float32')
else:
calib -= self.parent.det.pedestals(self.parent.evt)
elif self.parent.exp.image_property == self.parent.exp.disp_raw: # raw
calib = self.parent.det.raw(self.parent.evt)
if calib is None:
calib = np.zeros_like(self.parent.exp.detGuaranteed,
dtype='float32')
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_photons: # photon counts
calib = self.parent.det.photons(
self.parent.evt,
mask=self.parent.mk.userMask,
adu_per_photon=self.parent.exp.aduPerPhoton)
if calib is None:
calib = np.zeros_like(self.parent.exp.detGuaranteed,
dtype='int32')
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_pedestal: # pedestal
calib = self.parent.det.pedestals(self.parent.evt)
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_status: # status
calib = self.parent.det.status(self.parent.evt)
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_rms: # rms
calib = self.parent.det.rms(self.parent.evt)
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_commonMode: # common mode
calib = self.getCommonMode(evtNumber)
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_gain: # gain
calib = self.parent.det.gain(self.parent.evt)
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_gainMask: # gain_mask
calib = self.parent.det.gain_mask(self.parent.evt)
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_coordx: # coords_x
calib = self.parent.det.coords_x(self.parent.evt)
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_coordy: # coords_y
calib = self.parent.det.coords_y(self.parent.evt)
self.parent.firstUpdate = True
shape = self.parent.det.shape(self.parent.evt)
if len(shape) == 3:
if self.parent.exp.image_property == self.parent.exp.disp_quad: # quad ind
calib = np.zeros(shape)
for i in range(shape[0]):
# TODO: handle detectors properly
if shape[0] == 32: # cspad
calib[i, :, :] = int(i) % 8
elif shape[0] == 2: # cspad2x2
calib[i, :, :] = int(i) % 2
elif shape[0] == 4: # pnccd
calib[i, :, :] = int(i) % 4
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_seg: # seg ind
calib = np.zeros(shape)
if shape[0] == 32: # cspad
for i in range(32):
calib[i, :, :] = int(i) / 8
elif shape[0] == 2: # cspad2x2
for i in range(2):
calib[i, :, :] = int(i)
elif shape[0] == 4: # pnccd
for i in range(4):
calib[i, :, :] = int(i)
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_row: # row ind
calib = np.zeros(shape)
if shape[0] == 32: # cspad
for i in range(185):
calib[:, i, :] = i
elif shape[0] == 2: # cspad2x2
for i in range(185):
calib[:, i, :] = i
elif shape[0] == 4: # pnccd
for i in range(512):
calib[:, i, :] = i
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_col: # col ind
calib = np.zeros(shape)
if shape[0] == 32: # cspad
for i in range(388):
calib[:, :, i] = i
elif shape[0] == 2: # cspad2x2
for i in range(388):
calib[:, :, i] = i
elif shape[0] == 4: # pnccd
for i in range(512):
calib[:, :, i] = i
self.parent.firstUpdate = True
# Update photon energy
self.parent.exp.updatePhotonEnergy('lcls')
# Update clen
self.parent.geom.updateClen('lcls')
# Write a temporary geom file
self.parent.geom.deployCrystfelGeometry('lcls')
self.parent.geom.writeCrystfelGeom('lcls') # Hack to override coffset
# Get assembled image
if calib is not None:
data = self.getAssembledImage('lcls', calib)
else:
calib = np.zeros_like(self.parent.exp.detGuaranteed,
dtype='float32')
data = self.getAssembledImage('lcls', calib)
# Update detector centre
self.updateDetectorCentre('lcls')
# Update ROI histogram
if self.parent.roi.roiCurrent == 'rect':
self.parent.roi.updateRoi(self.parent.roi.roi)
elif self.parent.roi.roiCurrent == 'poly':
self.parent.roi.updateRoi(self.parent.roi.roiPoly)
elif self.parent.roi.roiCurrent == 'circ':
self.parent.roi.updateRoi(self.parent.roi.roiCircle)
return calib, data
def getCentre(self, shape):
cx = shape[1] / 2
cy = shape[0] / 2
return cx, cy
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,
generousMask=0,
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.generousMaskOn = generousMask
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
self.generousMask = None
# Make user mask
if self.userMask_path is not None:
self.userMask = np.load(self.userMask_path)
if facility == 'LCLS':
# 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)
if self.generousMaskOn:
self.psanaMask = self.generousBadPixel(self.psanaMask)
# Combine userMask and psanaMask
self.userPsanaMask = np.ones_like(self.det.calib(evt),
dtype=np.int16)
if self.userMask is not None:
self.userPsanaMask *= self.userMask
if self.psanaMask is not None:
self.userPsanaMask *= self.psanaMask
elif facility == 'PAL':
self.userPsanaMask = self.userMask
# Powder of hits and misses
self.powderHits = None
self.powderMisses = None
# 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 == 2:
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"]
if facility == 'LCLS':
self.access = kwargs["access"]
if self.algorithm == 1:
self.alg = PyAlgos(mask=None, pbits=0)
self.peakRadius = int(self.hitParam_alg1_radius)
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 = myskbeam.DropletA(self.peakRadius, self.peakRadius+self.hitParam_alg1_dr) #PyAlgos(windows=self.windows, mask=None, pbits=0)
elif self.algorithm == 2:
# set peak-selector parameters:
self.alg = PyAlgos(mask=None, pbits=0)
self.peakRadius = int(self.hitParam_alg1_radius)
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)
elif facility == 'PAL':
self.peakRadius = int(self.hitParam_alg1_radius)
self.alg = myskbeam.DropletA(self.peakRadius,
self.hitParam_alg1_dr)
if facility == 'LCLS':
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()
elif facility == 'PAL':
self.geom = kwargs["geom"]
with open(self.geom, 'r') as f:
lines = f.readlines()
for i, line in enumerate(lines):
if 'p0/max_ss' in line:
dim0 = int(line.split('=')[-1]) + 1
elif 'p0/max_fs' in line:
dim1 = int(line.split('=')[-1]) + 1
elif 'p0/corner_x' in line:
self.cy = -1 * float(line.split('=')[-1])
elif 'p0/corner_y' in line:
self.cx = float(line.split('=')[-1])
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)
def generousBadPixel(self, unassemMask, n=10):
generousBadPixelMask = unassemMask
(numAsic, numFs, numSs) = unassemMask.shape
for i in range(numAsic):
for a in range(numSs):
numBadPixels = len(np.where(unassemMask[i, :, a] == 0)[0])
if numBadPixels >= n:
generousBadPixelMask[i, :, a] = 0
return generousBadPixelMask
def setupExperiment(self):
access = 'exp=' + str(self.exp) + ':run=' + str(self.run) + ':idx'
if 'ffb' in self.access.lower():
access += ':dir=/reg/d/ffb/' + self.exp[:3] + '/' + self.exp + '/xtc'
self.ds = psana.DataSource(access)
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, thr_high=None, thr_low=None):
if facility == 'LCLS':
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.set_mask(self.combinedMask) # This doesn't work reliably
elif facility == 'PAL':
self.combinedMask = self.userMask
# set algorithm specific parameters
if self.algorithm == 1:
if facility == 'LCLS':
#print "param: ", self.npix_min, self.npix_max, self.atot_thr, self.son_min, thr_low, thr_high, np.sum(self.combinedMask)
# v1 - aka Droplet Finder - two-threshold peak-finding algorithm in restricted region
# around pixel with maximal intensity.
if thr_high is None: # use gui input
self.peakRadius = int(self.hitParam_alg1_radius)
self.peaks = self.alg.peak_finder_v4r3(
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,
mask=self.combinedMask.astype(np.uint16))
# 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)
else:
self.peaks = self.alg.findPeaks(calib,
npix_min=self.npix_min,
npix_max=self.npix_max,
atot_thr=self.atot_thr,
son_min=self.son_min,
thr_low=thr_low,
thr_high=thr_high,
mask=self.combinedMask)
# self.peaks = self.alg.peak_finder_v4r2(calib,
# thr_low=thr_low,
# thr_high=thr_high,
# rank=self.hitParam_alg1_rank,
# r0=self.hitParam_alg1_radius,
# dr=self.hitParam_alg1_dr)
elif facility == 'PAL':
self.peakRadius = int(self.hitParam_alg1_radius)
_calib = np.zeros((1, calib.shape[0], calib.shape[1]))
_calib[0, :, :] = calib
if self.combinedMask is None:
_mask = None
else:
_mask = self.combinedMask.astype(np.uint16)
self.peaks = self.alg.findPeaks(
_calib,
npix_min=self.npix_min,
npix_max=self.npix_max,
son_min=self.son_min,
thr_low=self.hitParam_alg1_thr_low,
thr_high=self.hitParam_alg1_thr_high,
atot_thr=self.atot_thr,
r0=self.peakRadius,
dr=int(self.hitParam_alg1_dr),
mask=_mask)
elif self.algorithm == 2:
if facility == 'LCLS':
#print "param: ", self.npix_min, self.npix_max, self.atot_thr, self.son_min, thr_low, thr_high, np.sum(self.combinedMask)
# 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_v3r3(
calib,
rank=int(self.hitParam_alg1_rank),
r0=self.peakRadius,
dr=self.hitParam_alg1_dr,
nsigm=self.son_min,
mask=self.combinedMask.astype(np.uint16))
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:
if facility == 'LCLS':
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
elif facility == 'PAL':
cenX = self.iX[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[:, 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:
if self.powderHits is None:
self.powderHits = calib
else:
self.powderHits = np.maximum(self.powderHits, calib)
else:
if self.powderMisses is None:
self.powderMisses = calib
else:
self.powderMisses = np.maximum(self.powderMisses, calib)
if self.powderHits is None: self.powderHits = np.zeros_like(calib)
if self.powderMisses is None: self.powderMisses = np.zeros_like(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)
if facility == 'LCLS':
# Make psana mask
self.psanaMask = detector.mask(run,
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.psanaMask, dtype=np.int16)
if self.userMask is not None:
self.userPsanaMask *= self.userMask
if self.psanaMask_on:
self.userPsanaMask *= self.psanaMask
# Powder of hits and misses
self.powderHits = None
self.powderMisses = None
# 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 >= 2:
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"]
if facility == 'LCLS':
self.access = kwargs["access"]
if self.algorithm == 1:
self.alg = PyAlgos(mask=None, pbits=0)
self.peakRadius = int(self.hitParam_alg1_radius)
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)
elif self.algorithm >= 2:
self.alg = PyAlgos(mask=None, pbits=0)
self.peakRadius = int(self.hitParam_alg1_radius)
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 facility == 'LCLS':
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))
try:
self.cy, self.cx = self.det.point_indexes(
evt,
pxy_um=(0, 0),
pix_scale_size_um=None,
xy0_off_pix=None,
cframe=gu.CFRAME_PSANA,
fract=True)
except AttributeError:
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):
access = 'exp=' + str(self.exp) + ':run=' + str(self.run) + ':idx'
if 'ffb' in self.access.lower():
access += ':dir=/reg/d/ffb/' + self.exp[:3] + '/' + self.exp + '/xtc'
self.ds = psana.DataSource(access)
self.run = next(self.ds.runs())
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=0o0377
) # 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):
# FIXME: handle vertical/horizontal polarization properly
self.pf = polarization_factor(self.rb.pixel_rad(),
self.rb.pixel_phi() + 90,
self.distance * 1e6) # convert to um
def findPeaks(self, calib, evt, minPeaks=15, thr_high=None, thr_low=None):
#t0 = time.time()
if facility == 'LCLS':
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
self.calib = calib # save background subtracted calib as an attribute
if self.streakMask is not None:
self.combinedMask = self.userPsanaMask * self.streakMask
else:
self.combinedMask = self.userPsanaMask
#t1 = time.time()
# set algorithm specific parameters
if self.algorithm == 1:
if facility == 'LCLS':
# v1 - aka Droplet Finder - two-threshold peak-finding algorithm in restricted region
# around pixel with maximal intensity.
if thr_high is None: # use gui input
self.peakRadius = int(self.hitParam_alg1_radius)
self.peaks = self.alg.peak_finder_v4r3(
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,
mask=self.combinedMask.astype(np.uint16))
else:
self.peaks = self.alg.findPeaks(calib,
npix_min=self.npix_min,
npix_max=self.npix_max,
atot_thr=self.atot_thr,
son_min=self.son_min,
thr_low=thr_low,
thr_high=thr_high,
mask=self.combinedMask)
elif self.algorithm == 2:
if facility == 'LCLS':
# Adaptive peak finder v3r3
self.peakRadius = int(self.hitParam_alg1_radius)
self.peaks = self.alg.peak_finder_v3r3(
calib,
rank=int(self.hitParam_alg1_rank),
r0=self.peakRadius,
dr=self.hitParam_alg1_dr,
nsigm=self.son_min,
mask=self.combinedMask.astype(np.uint16))
elif self.algorithm == 3:
if facility == 'LCLS':
# perform binning here
binr = 2
binc = 2
downCalib = sm.block_reduce(calib,
block_size=(1, binr, binc),
func=np.sum)
downWeight = sm.block_reduce(self.combinedMask,
block_size=(1, binr, binc),
func=np.sum)
warr = np.zeros_like(downCalib, dtype='float32')
ind = np.where(downWeight > 0)
warr[ind] = downCalib[ind] / downWeight[ind]
upCalib = utils.upsample(warr, calib.shape, binr, binc)
self.peakRadius = int(self.hitParam_alg1_radius)
self.peaks = self.alg.peak_finder_v3r3(
upCalib,
rank=int(self.hitParam_alg1_rank),
r0=self.peakRadius,
dr=self.hitParam_alg1_dr,
nsigm=self.son_min,
mask=self.combinedMask.astype(np.uint16))
#t2 = time.time()
self.numPeaksFound = self.peaks.shape[0]
if self.numPeaksFound >= minPeaks:
if facility == 'LCLS':
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
#t3 = time.time()
if self.numPeaksFound >= minPeaks:
if self.powderHits is None:
self.powderHits = calib
else:
self.powderHits = np.maximum(self.powderHits, calib)
else:
if self.powderMisses is None:
self.powderMisses = calib
else:
self.powderMisses = np.maximum(self.powderMisses, calib)
if self.powderHits is None: self.powderHits = np.zeros_like(calib)
if self.powderMisses is None: self.powderMisses = np.zeros_like(calib)
class psanaWhisperer():
def __init__(self,
experimentName,
runNumber,
detInfo,
clen='',
aduPerPhoton=1,
localCalib=False):
self.experimentName = experimentName
self.runNumber = runNumber
self.detInfo = detInfo
self.clenStr = clen
self.aduPerPhoton = aduPerPhoton
self.localCalib = localCalib
def setupExperiment(self):
self.ds = psana.DataSource('exp=' + str(self.experimentName) +
':run=' + str(self.runNumber) + ':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.detInfo), self.env)
self.det.do_reshape_2d_to_3d(flag=True)
self.getDetInfoList()
self.detAlias = self.getDetectorAlias(str(self.detInfo))
self.updateClen() # Get epics variable, clen
def updateClen(self):
if 'cspad' in self.detInfo.lower() and 'cxi' in self.experimentName:
self.epics = self.ds.env().epicsStore()
self.clen = self.epics.value(self.clenStr)
elif 'rayonix' in self.detInfo.lower(
) and 'mfx' in self.experimentName:
self.epics = self.ds.env().epicsStore()
self.clen = self.epics.value(self.clenStr)
elif 'rayonix' in self.detInfo.lower(
) and 'xpp' in self.experimentName:
self.epics = self.ds.env().epicsStore()
self.clen = self.epics.value(self.clenStr)
def getDetectorAlias(self, srcOrAlias):
for i in self.detInfoList:
src, alias, _ = i
if srcOrAlias.lower() == src.lower() or srcOrAlias.lower(
) == alias.lower():
return alias
def getDetInfoList(self):
myAreaDetectors = []
self.detnames = psana.DetNames()
for k in self.detnames:
try:
if Detector.PyDetector.dettype(str(
k[0]), self.env) == Detector.AreaDetector.AreaDetector:
myAreaDetectors.append(k)
except ValueError:
continue
self.detInfoList = list(set(myAreaDetectors))
def getEvent(self, number):
self.evt = self.run.event(self.times[number])
def getImg(self, number):
self.getEvent(number)
img = self.det.image(self.evt, self.det.calib(self.evt))
return img
def getImg(self):
if self.evt is not None:
img = self.det.image(self.evt, self.det.calib(self.evt))
return img
return None
def getCheetahImg(self, calib=None):
"""Converts seg, row, col assuming (32,185,388)
to cheetah 2-d table row and col (8*185, 4*388)
"""
if 'cspad2x2' in self.detInfo.lower():
print "Not implemented yet: cspad2x2"
elif 'cspad' in self.detInfo.lower():
if calib is None: calib = self.det.calib(self.evt) # (32,185,388)
img = np.zeros((8 * 185, 4 * 388))
counter = 0
for quad in range(4):
for seg in range(8):
img[seg * 185:(seg + 1) * 185,
quad * 388:(quad + 1) * 388] = calib[counter, :, :]
counter += 1
elif 'rayonix' in self.detInfo.lower():
if calib is None:
img = np.squeeze(self.det.calib(self.evt)) # (1920,1920)
else:
img = np.squeeze(calib)
return img
def getCleanAssembledImg(self, backgroundEvent):
"""Returns psana assembled image
"""
backgroundEvt = self.run.event(self.times[backgroundEvent])
backgroundCalib = self.det.calib(backgroundEvt)
calib = self.det.calib(self.evt)
cleanCalib = calib - backgroundCalib
img = self.det.image(self.evt, cleanCalib)
return img
def getAssembledImg(self):
"""Returns psana assembled image
"""
img = self.det.image(self.evt)
return img
def getCalibImg(self):
"""Returns psana assembled image
"""
img = self.det.calib(self.evt)
return img
def getCleanAssembledPhotons(self, backgroundEvent):
"""Returns psana assembled image in photon counts
"""
backgroundEvt = self.run.event(self.times[backgroundEvent])
backgroundCalib = self.det.calib(backgroundEvt)
calib = self.det.calib(self.evt)
cleanCalib = calib - backgroundCalib
img = self.det.photons(self.evt,
nda_calib=cleanCalib,
adu_per_photon=self.aduPerPhoton)
phot = self.det.image(self.evt, img)
return phot
def getAssembledPhotons(self):
"""Returns psana assembled image in photon counts
"""
img = self.det.photons(self.evt, adu_per_photon=self.aduPerPhoton)
phot = self.det.image(self.evt, img)
return phot
def getPsanaEvent(self, cheetahFilename):
# Gets psana event given cheetahFilename, e.g. LCLS_2015_Jul26_r0014_035035_e820.h5
hrsMinSec = cheetahFilename.split('_')[-2]
fid = int(cheetahFilename.split('_')[-1].split('.')[0], 16)
for t in self.times:
if t.fiducial() == fid:
localtime = time.strftime('%H:%M:%S',
time.localtime(t.seconds()))
localtime = localtime.replace(':', '')
if localtime[0:3] == hrsMinSec[0:3]:
self.evt = self.run.event(t)
else:
self.evt = None
def getStartTime(self):
self.evt = self.run.event(self.times[0])
evtId = self.evt.get(psana.EventId)
sec = evtId.time()[0]
nsec = evtId.time()[1]
fid = evtId.fiducials()
return time.strftime('%FT%H:%M:%S-0800',
time.localtime(sec)) # Hard-coded pacific time
#####################################################################
# TODO: Functions below are not being used yet
#####################################################################
def findPsanaGeometry(self):
try:
self.source = psana.Detector.PyDetector.map_alias_to_source(
self.detInfo, self.ds.env()) # 'DetInfo(CxiDs2.0:Cspad.0)'
self.calibSource = self.source.split('(')[-1].split(')')[
0] # 'CxiDs2.0:Cspad.0'
self.detectorType = gu.det_type_from_source(self.source) # 1
self.calibGroup = gu.dic_det_type_to_calib_group[
self.detectorType] # 'CsPad::CalibV1'
self.detectorName = gu.dic_det_type_to_name[
self.detectorType].upper() # 'CSPAD'
if self.localCalib:
self.calibPath = "./calib/" + self.calibGroup + "/" + self.calibSource + "/geometry"
else:
self.calibPath = "/reg/d/psdm/" + self.parent.experimentName[0:3] + \
"/" + self.parent.experimentName + "/calib/" + \
self.calibGroup + "/" + self.calibSource + "/geometry"
# Determine which calib file to use
geometryFiles = os.listdir(self.calibPath)
self.calibFile = None
minDiff = -1e6
for fname in geometryFiles:
if fname.endswith('.data'):
endValid = False
startNum = int(fname.split('-')[0])
endNum = fname.split('-')[-1].split('.data')[0]
diff = startNum - self.parent.runNumber
# Make sure it's end number is valid too
if 'end' in endNum:
endValid = True
else:
try:
if self.parent.runNumber <= int(endNum):
endValid = True
except:
continue
if diff <= 0 and diff > minDiff and endValid is True:
minDiff = diff
self.calibFile = fname
except:
if self.parent.args.v >= 1: print "Couldn't find psana geometry"
self.calibFile = None
def setupRadialBackground(self):
self.findPsanaGeometry()
if self.calibFile is not None:
self.geo = GeometryAccess(self.calibPath + '/' + self.calibFile)
self.xarr, self.yarr, self.zarr = self.geo.get_pixel_coords()
self.iX, self.iY = self.geo.get_pixel_coord_indexes()
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)
else:
self.rb = None
def updatePolarizationFactor(self, detectorDistance_in_m):
if self.rb is not None:
self.pf = polarization_factor(self.rb.pixel_rad(),
self.rb.pixel_phi(),
detectorDistance_in_m *
1e6) # convert to um
def getCalib(self, evtNumber):
if self.run is not None:
self.evt = self.getEvent(evtNumber)
if self.applyCommonMode: # play with different common mode
if self.commonMode[0] == 5: # Algorithm 5
calib = self.det.calib(self.evt,
cmpars=(self.commonMode[0],
self.commonMode[1]))
else: # Algorithms 1 to 4
print "### Overriding common mode: ", self.commonMode
calib = self.det.calib(
self.evt,
cmpars=(self.commonMode[0], self.commonMode[1],
self.commonMode[2], self.commonMode[3]))
else:
calib = self.det.calib(self.evt)
return calib
else:
return None
def getPreprocessedImage(self, evtNumber, image_property):
disp_medianCorrection = 19
disp_radialCorrection = 18
disp_gainMask = 17
disp_coordy = 16
disp_coordx = 15
disp_col = 14
disp_row = 13
disp_seg = 12
disp_quad = 11
disp_gain = 10
disp_commonMode = 9
disp_rms = 8
disp_status = 7
disp_pedestal = 6
disp_photons = 5
disp_raw = 4
disp_pedestalCorrected = 3
disp_commonModeCorrected = 2
disp_adu = 1
if image_property == disp_medianCorrection: # median subtraction
print "Sorry, this feature isn't available yet"
elif image_property == disp_radialCorrection: # radial subtraction + polarization corrected
self.getEvent(evtNumber)
calib = self.getCalib(evtNumber)
if calib:
self.pf.shape = self.parent.calib.shape
calib = self.rb.subtract_bkgd(calib * self.pf)
elif image_property == disp_adu: # gain and hybrid gain corrected
calib = self.getCalib(evtNumber)
elif image_property == disp_commonModeCorrected: # common mode corrected
calib = self.getCommonModeCorrected(evtNumber)
elif image_property == disp_pedestalCorrected: # pedestal corrected
calib = self.det.raw(self.evt).astype('float32')
if calib: calib -= self.det.pedestals(self.evt)
elif image_property == disp_raw: # raw
calib = self.det.raw(self.evt)
elif image_property == disp_photons: # photon counts
calib = self.det.photons(
self.evt,
mask=self.parent.mk.userMask,
adu_per_photon=self.parent.exp.aduPerPhoton)
if calib is None:
calib = np.zeros_like(self.parent.exp.detGuaranteed,
dtype='int32')
elif image_property == disp_pedestal: # pedestal
calib = self.parent.det.pedestals(self.parent.evt)
elif image_property == disp_status: # status
calib = self.parent.det.status(self.parent.evt)
elif image_property == disp_rms: # rms
calib = self.parent.det.rms(self.parent.evt)
elif image_property == disp_commonMode: # common mode
calib = self.getCommonMode(evtNumber)
elif image_property == disp_gain: # gain
calib = self.parent.det.gain(self.parent.evt)
elif image_property == disp_gainMask: # gain_mask
calib = self.parent.det.gain_mask(self.parent.evt)
elif image_property == disp_coordx: # coords_x
calib = self.parent.det.coords_x(self.parent.evt)
elif image_property == disp_coordy: # coords_y
calib = self.parent.det.coords_y(self.parent.evt)
shape = self.parent.det.shape(self.parent.evt)
if len(shape) == 3:
if image_property == disp_quad: # quad ind
calib = np.zeros(shape)
for i in range(shape[0]):
# FIXME: handle detectors properly
if shape[0] == 32: # cspad
calib[i, :, :] = int(i) % 8
elif shape[0] == 2: # cspad2x2
calib[i, :, :] = int(i) % 2
elif shape[0] == 4: # pnccd
calib[i, :, :] = int(i) % 4
elif image_property == disp_seg: # seg ind
calib = np.zeros(shape)
if shape[0] == 32: # cspad
for i in range(32):
calib[i, :, :] = int(i) / 8
elif shape[0] == 2: # cspad2x2
for i in range(2):
calib[i, :, :] = int(i)
elif shape[0] == 4: # pnccd
for i in range(4):
calib[i, :, :] = int(i)
elif image_property == disp_row: # row ind
calib = np.zeros(shape)
if shape[0] == 32: # cspad
for i in range(185):
calib[:, i, :] = i
elif shape[0] == 2: # cspad2x2
for i in range(185):
calib[:, i, :] = i
elif shape[0] == 4: # pnccd
for i in range(512):
calib[:, i, :] = i
elif image_property == disp_col: # col ind
calib = np.zeros(shape)
if shape[0] == 32: # cspad
for i in range(388):
calib[:, :, i] = i
elif shape[0] == 2: # cspad2x2
for i in range(388):
calib[:, :, i] = i
elif shape[0] == 4: # pnccd
for i in range(512):
calib[:, :, i] = i
class ImageViewer(object):
def __init__(self, parent=None):
self.parent = parent
self.maxPercentile = 0
self.minPercentile = 0
self.displayMaxPercentile = 99.8
self.displayMinPercentile = 1.0
self.rb = None
pg.setConfigOptions(imageAxisOrder="row-major")
## Dock 2: Image Panel
self.dock = Dock("Image Panel", size=(500, 400))
self.win = pg.ImageView(view=pg.PlotItem())
self.win.getView().invertY(True)
self.img_feature = pg.ImageItem()
# Set color map white -> black
#colors = [
# (255, 255, 255),
# (0, 0, 0)
#]
#self.cmap = pg.ColorMap(pos=np.linspace(0.0, 1.0, len(colors)), color=colors)
#self.win.setColorMap(self.cmap)
self.win.getView().addItem(self.img_feature)
self.ring_feature = pg.ScatterPlotItem()
self.centre_feature = pg.ScatterPlotItem()
self.peak_feature = pg.ScatterPlotItem()
self.indexedPeak_feature = pg.ScatterPlotItem()
self.filePeak_feature = pg.ScatterPlotItem()
self.z_direction = pg.ScatterPlotItem()
self.z_direction1 = pg.ScatterPlotItem()
self.win.getView().addItem(self.ring_feature)
self.win.getView().addItem(self.centre_feature)
self.win.getView().addItem(self.peak_feature)
self.win.getView().addItem(self.indexedPeak_feature)
self.win.getView().addItem(self.filePeak_feature)
self.win.getView().addItem(self.z_direction)
self.win.getView().addItem(self.z_direction1)
self.abc_text = pg.TextItem(html='',
anchor=(0, 0)) # unit cell display
self.win.getView().addItem(self.abc_text)
self.peak_text = pg.TextItem(html='', anchor=(0, 0)) # peak display
self.win.getView().addItem(self.peak_text)
self.dock.addWidget(self.win)
self.drawLabCoordinates()
def clearPeakMessage(self):
self.win.getView().removeItem(self.peak_text)
self.peak_feature.setData([], [], pxMode=False)
if self.parent.args.v >= 1: print("Done clearPeakMessage")
def clearIndexingMessage(self):
self.win.getView().removeItem(self.abc_text)
self.indexedPeak_feature.setData([], [], pxMode=False)
if self.parent.args.v >= 1: print("Done clearIndexingMessage")
def updateIndexingMessage(self, abc_text):
self.win.getView().addItem(abc_text)
def drawLabCoordinates(self):
(cenX, cenY) = (-20, -20) # no offset
# Draw xy arrows
symbolSize = 40
cutoff = symbolSize / 2
headLen = -30
tailLen = -30 + cutoff
xArrow = pg.ArrowItem(angle=180,
tipAngle=30,
baseAngle=20,
headLen=headLen,
tailLen=tailLen,
tailWidth=8,
pen=None,
brush='b',
pxMode=False)
xArrow.setPos(2 * headLen + cenY, 0 + cenX)
self.win.getView().addItem(xArrow)
yArrow = pg.ArrowItem(angle=-90,
tipAngle=30,
baseAngle=20,
headLen=headLen,
tailLen=tailLen,
tailWidth=8,
pen=None,
brush='r',
pxMode=False)
yArrow.setPos(0 + cenY, 2 * headLen + cenX)
self.win.getView().addItem(yArrow)
# Lab coordinates: Add z-direction
self.z_direction.setData([0+cenY], [0+cenX], symbol='o', \
size=symbolSize, brush='w', \
pen={'color': 'k', 'width': 4}, pxMode=False)
self.z_direction1.setData([0+cenY], [0+cenX], symbol='x', \
size=symbolSize, brush='k', \
pen={'color': 'k', 'width': 4}, pxMode=False)
# Lab coordinates: Add xyz text
self.x_text = pg.TextItem(
html=
'<div style="text-align: left"><span style="color: #0000FF; font-size: 16pt;">x</span></div>',
anchor=(0, 0))
self.win.getView().addItem(self.x_text)
self.x_text.setPos(2 * headLen + cenY, 0 + cenX)
self.y_text = pg.TextItem(
html=
'<div style="text-align: left"><span style="color: #FF0000; font-size: 16pt;">y</span></div>',
anchor=(1, 1))
self.win.getView().addItem(self.y_text)
self.y_text.setPos(0 + cenY, 2 * headLen + cenX)
self.z_text = pg.TextItem(
html=
'<div style="text-align: left"><span style="color: #000000; font-size: 16pt;">z</span></div>',
anchor=(1, 0))
self.win.getView().addItem(self.z_text)
self.z_text.setPos(-headLen + cenY, 0 + cenX)
# Label xy axes
self.x_axis = self.win.getView().getAxis('bottom')
self.x_axis.setLabel('X-axis (pixels)')
self.y_axis = self.win.getView().getAxis('left')
self.y_axis.setLabel('Y-axis (pixels)')
def updateImage(self, calib=None):
if self.parent.hasExperimentName and self.parent.hasRunNumber and self.parent.hasDetInfo:
if calib is None:
self.parent.calib, self.parent.data = self.getDetImage(
self.parent.eventNumber)
else:
_, self.parent.data = self.getDetImage(self.parent.eventNumber,
calib=calib)
if self.parent.firstUpdate:
if self.parent.exp.logscaleOn:
self.win.setImage(
np.log10(abs(self.parent.data) + self.parent.eps))
self.win.view.setRange(
xRange=[0, self.parent.data.shape[0]],
yRange=[0, self.parent.data.shape[1]],
padding=0)
self.parent.firstUpdate = False
else:
self.minPercentile = np.percentile(
self.parent.data, self.displayMinPercentile)
if self.minPercentile < 0: self.minPercentile = 0
self.maxPercentile = np.percentile(
self.parent.data, self.displayMaxPercentile)
self.win.setImage(self.parent.data,
levels=(self.minPercentile,
self.maxPercentile))
# properly resize the image to fit on screen
self.win.view.setRange(
xRange=[0, self.parent.data.shape[0]],
yRange=[0, self.parent.data.shape[1]],
padding=0)
self.parent.firstUpdate = False
else:
if self.parent.exp.logscaleOn:
self.win.setImage(
np.log10(abs(self.parent.data) + self.parent.eps),
autoRange=False,
autoLevels=False,
autoHistogramRange=False)
else:
if self.minPercentile == 0 and self.maxPercentile == 0:
self.minPercentile = np.percentile(
self.parent.data, self.displayMinPercentile)
if self.minPercentile < 0: self.minPercentile = 0
self.maxPercentile = np.percentile(
self.parent.data, self.displayMaxPercentile)
self.win.setImage(self.parent.data,
levels=(self.minPercentile,
self.maxPercentile))
else:
self.win.setImage(self.parent.data,
autoRange=False,
autoLevels=False,
autoHistogramRange=False)
# Load peak parameters if exists
if 'sfx' in self.parent.args.mode and self.parent.pk.userUpdate is None:
self.parent.pk.updateParam()
else:
self.parent.mk.displayMask()
if 'label' in self.parent.args.mode:
self.parent.labeling.updateText()
if self.parent.args.v >= 1: print("Done updateImage")
def getCalib(self, evtNumber):
if self.parent.exp.run is not None:
self.parent.evt = self.parent.exp.getEvt(evtNumber)
if self.parent.exp.applyCommonMode: # play with different common mode
if self.parent.exp.commonMode[0] == 5: # Algorithm 5
calib = self.parent.det.calib(
self.parent.evt,
cmpars=(self.parent.exp.commonMode[0],
self.parent.exp.commonMode[1]))
else: # Algorithms 1 to 4
print("### Overriding common mode: ",
self.parent.exp.commonMode)
calib = self.parent.det.calib(
self.parent.evt,
cmpars=(self.parent.exp.commonMode[0],
self.parent.exp.commonMode[1],
self.parent.exp.commonMode[2],
self.parent.exp.commonMode[3]))
else:
if not self.parent.inputImages:
calib = self.parent.det.calib(self.parent.evt)
else:
f = h5py.File(self.parent.inputImages)
ind = np.where(f['eventNumber'][()] == evtNumber)[0][0]
if len(f['/data/data'].shape) == 3:
calib = ipct(self.parent.detInfo,
f['data/data'][ind, :, :])
else:
calib = f['data/data'][ind, :, :, :]
f.close()
return calib
else:
return None
def getCommonModeCorrected(self, evtNumber):
if self.parent.exp.run is not None:
try:
self.parent.evt = self.parent.exp.getEvt(evtNumber)
pedestalCorrected = self.parent.det.raw(
self.parent.evt) - self.parent.det.pedestals(
self.parent.evt)
if self.parent.exp.applyCommonMode: # play with different common mode
if self.parent.exp.commonMode[0] == 5: # Algorithm 5
commonMode = self.parent.det.common_mode_correction(
self.parent.evt,
pedestalCorrected,
cmpars=(self.parent.exp.commonMode[0],
self.parent.exp.commonMode[1]))
commonModeCorrected = pedestalCorrected - commonMode
else: # Algorithms 1 to 4
print("### Overriding common mode: ",
self.parent.exp.commonMode)
commonMode = self.parent.det.common_mode_correction(
self.parent.evt,
pedestalCorrected,
cmpars=(self.parent.exp.commonMode[0],
self.parent.exp.commonMode[1],
self.parent.exp.commonMode[2],
self.parent.exp.commonMode[3]))
commonModeCorrected = pedestalCorrected - commonMode
else:
commonMode = self.parent.det.common_mode_correction(
self.parent.evt, pedestalCorrected)
commonModeCorrected = pedestalCorrected + commonMode # WHAT! You need to ADD common mode?!!
return commonModeCorrected
except:
return None
else:
return None
def getCommonMode(self, evtNumber):
if self.parent.exp.run is not None:
self.parent.evt = self.parent.exp.getEvt(evtNumber)
pedestalCorrected = self.parent.det.raw(
self.parent.evt) - self.parent.det.pedestals(self.parent.evt)
if self.parent.exp.applyCommonMode: # play with different common mode
print("### Overriding common mode: ",
self.parent.exp.commonMode)
if self.parent.exp.commonMode[0] == 5: # Algorithm 5
cm = self.parent.det.common_mode_correction(
self.parent.evt,
pedestalCorrected,
cmpars=(self.parent.exp.commonMode[0],
self.parent.exp.commonMode[1]))
else: # Algorithms 1 to 4
cm = self.parent.det.common_mode_correction(
self.parent.evt,
pedestalCorrected,
cmpars=(self.parent.exp.commonMode[0],
self.parent.exp.commonMode[1],
self.parent.exp.commonMode[2],
self.parent.exp.commonMode[3]))
else:
cm = self.parent.det.common_mode_correction(
self.parent.evt, pedestalCorrected)
return cm
else:
return None
def getAssembledImage(self, arg, calib):
_calib = calib.copy() # this is important
tic = time.time()
if self.parent.exp.applyFriedel: # Apply Friedel symmetry
print("Apply Friedel symmetry")
try:
centre = self.parent.det.point_indexes(self.parent.evt,
pxy_um=(0, 0),
pix_scale_size_um=None,
xy0_off_pix=None,
cframe=gu.CFRAME_PSANA,
fract=True)
except AttributeError:
centre = self.parent.det.point_indexes(self.parent.evt)
self.fs = FriedelSym(self.parent.exp.detGuaranteedData.shape,
centre)
data = self.parent.det.image(self.parent.evt, _calib)
if self.parent.mk.combinedMask is not None:
data = self.fs.applyFriedel(data,
mask=self.parent.det.image(
self.parent.evt,
self.parent.mk.combinedMask),
mode='same')
else:
data = self.fs.applyFriedel(data, mask=None, mode='same')
else:
data = self.parent.det.image(self.parent.evt, _calib)
if data is None: data = _calib
toc = time.time()
if self.parent.args.v >= 1: print("time assemble: ", toc - tic)
return data
def setupRadialBackground(self):
self.parent.geom.findPsanaGeometry()
if self.parent.geom.calibFile is not None:
if self.parent.args.v >= 1:
print(
"calibFile: ", self.parent.geom.calibPath + '/' +
self.parent.geom.calibFile)
self.geo = self.parent.det.geometry(
self.parent.runNumber
) #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.iY = self.geo.get_pixel_coord_indexes()
self.mask = self.geo.get_pixel_mask(mbits=0o0377)
# 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)
if self.parent.args.v >= 1: print("Done setupRadialBackground")
else:
self.rb = None
def updatePolarizationFactor(
self): # FIXME: change to vertical polarization after July 2020
if self.rb is not None:
self.pf = polarization_factor(self.rb.pixel_rad(),
self.rb.pixel_phi() + 90,
self.parent.detectorDistance *
1e6) # convert to um
if self.parent.args.v >= 1: print("Done updatePolarizationFactor")
def updateDetectorCentre(self):
try:
self.parent.cy, self.parent.cx = self.parent.det.point_indexes(
self.parent.evt,
pxy_um=(0, 0),
pix_scale_size_um=None,
xy0_off_pix=None,
cframe=gu.CFRAME_PSANA,
fract=True)
except AttributeError:
self.parent.cy, self.parent.cx = self.parent.det.point_indexes(
self.parent.evt, pxy_um=(0, 0))
if self.parent.cx is None:
print("#######################################")
print(
"WARNING: Unable to get detector center position. Check detector geometry is deployed."
)
print("#######################################")
data = self.parent.det.image(self.parent.evt,
self.parent.exp.detGuaranteed)
self.parent.cx, self.parent.cy = self.getCentre(data.shape)
if self.parent.args.v >= 1:
print("cx, cy: ", self.parent.cx, self.parent.cy)
def getDetImage(self, evtNumber, calib=None):
if calib is None:
if self.parent.exp.image_property == self.parent.exp.disp_medianCorrection: # median subtraction
calib = self.getCalib(evtNumber)
if calib is None:
calib = np.zeros_like(self.parent.exp.detGuaranteed,
dtype='float32')
calib -= median_filter_ndarr(calib,
self.parent.exp.medianFilterRank)
elif self.parent.exp.image_property == self.parent.exp.disp_radialCorrection: # radial subtraction + polarization corrected
calib = self.getCalib(evtNumber)
if calib is None:
calib = np.zeros_like(self.parent.exp.detGuaranteed,
dtype='float32')
self.pf.shape = calib.shape # FIXME: shape is 1d
calib = self.rb.subtract_bkgd(calib * self.pf)
calib.shape = self.parent.calib.shape # FIXME: shape is 1d
elif self.parent.exp.image_property == self.parent.exp.disp_adu: # gain and hybrid gain corrected
calib = self.getCalib(evtNumber)
if calib is None:
calib = np.zeros_like(self.parent.exp.detGuaranteed,
dtype='float32')
elif self.parent.exp.image_property == self.parent.exp.disp_commonModeCorrected: # common mode corrected
calib = self.getCommonModeCorrected(evtNumber)
if calib is None:
calib = np.zeros_like(self.parent.exp.detGuaranteed,
dtype='float32')
elif self.parent.exp.image_property == self.parent.exp.disp_pedestalCorrected: # pedestal corrected
calib = self.parent.det.raw(self.parent.evt).astype('float32')
if calib is None:
calib = np.zeros_like(self.parent.exp.detGuaranteed,
dtype='float32')
else:
calib -= self.parent.det.pedestals(self.parent.evt)
elif self.parent.exp.image_property == self.parent.exp.disp_raw: # raw
calib = self.parent.det.raw(self.parent.evt)
if calib is None:
calib = np.zeros_like(self.parent.exp.detGuaranteed,
dtype='float32')
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_photons: # photon counts
calib = self.parent.det.photons(
self.parent.evt,
mask=self.parent.mk.userMask,
adu_per_photon=self.parent.exp.aduPerPhoton)
if calib is None:
calib = np.zeros_like(self.parent.exp.detGuaranteed,
dtype='int32')
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_pedestal: # pedestal
calib = self.parent.det.pedestals(self.parent.evt)
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_status: # status
calib = self.parent.det.status(self.parent.evt)
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_rms: # rms
calib = self.parent.det.rms(self.parent.evt)
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_commonMode: # common mode
calib = self.getCommonMode(evtNumber)
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_gain: # gain
calib = self.parent.det.gain(self.parent.evt)
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_gainMask: # gain_mask
calib = self.parent.det.gain_mask(self.parent.evt)
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_coordx: # coords_x
try:
calib = self.parent.det.coords_x(self.parent.evt,
cframe=1) # lab coords
except:
calib = self.parent.det.coords_x(
self.parent.evt) # matrix coords
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_coordy: # coords_y
try:
calib = self.parent.det.coords_y(self.parent.evt,
cframe=1) # lab coords
except:
calib = self.parent.det.coords_y(
self.parent.evt) # matrix coords
self.parent.firstUpdate = True
shape = self.parent.det.shape(self.parent.evt)
if len(shape) == 3:
if self.parent.exp.image_property == self.parent.exp.disp_seg: # seg ind
calib = np.zeros(shape)
for i in range(shape[0]):
calib[i, :, :] = i
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_row: # row ind
calib = np.zeros(shape)
for i in range(shape[1]):
calib[:, i, :] = i
self.parent.firstUpdate = True
elif self.parent.exp.image_property == self.parent.exp.disp_col: # col ind
calib = np.zeros(shape)
for i in range(shape[2]):
calib[:, :, i] = i
self.parent.firstUpdate = True
# Update photon energy
self.parent.exp.updatePhotonEnergy(self.parent.facility)
# Update clen
self.parent.geom.updateClen(self.parent.facility)
# Write a temporary geom file
#self.parent.geom.deployCrystfelGeometry(self.parent.facility)
#self.parent.geom.writeCrystfelGeom(self.parent.facility) # Hack to override coffset
# Get assembled image
if calib is not None:
data = self.getAssembledImage(self.parent.facility, calib)
else:
calib = np.zeros_like(self.parent.exp.detGuaranteed,
dtype='float32')
data = self.getAssembledImage(self.parent.facility, calib)
# Update ROI histogram
if self.parent.roi.roiCurrent == 'rect':
self.parent.roi.updateRoi(self.parent.roi.roi)
elif self.parent.roi.roiCurrent == 'poly':
self.parent.roi.updateRoi(self.parent.roi.roiPoly)
elif self.parent.roi.roiCurrent == 'circ':
self.parent.roi.updateRoi(self.parent.roi.roiCircle)
return calib, data
def getCentre(self, shape):
cx = shape[1] / 2
cy = shape[0] / 2
return cx, cy
class psanaWhisperer():
def __init__(self, experimentName, runNumber, detInfo, clen='', aduPerPhoton=1, localCalib=False):
self.experimentName = experimentName
self.runNumber = runNumber
self.detInfo = detInfo
self.clenStr = clen
self.aduPerPhoton = aduPerPhoton
self.localCalib = localCalib
def setupExperiment(self):
self.ds = psana.DataSource('exp=' + str(self.experimentName) + ':run=' + str(self.runNumber) + ':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.detInfo), self.env)
self.det.do_reshape_2d_to_3d(flag=True)
self.getDetInfoList()
self.detAlias = self.getDetectorAlias(str(self.detInfo))
self.updateClen() # Get epics variable, clen
def updateClen(self):
if 'cspad' in self.detInfo.lower() and 'cxi' in self.experimentName:
self.epics = self.ds.env().epicsStore()
self.clen = self.epics.value(self.clenStr)
elif 'rayonix' in self.detInfo.lower() and 'mfx' in self.experimentName:
self.epics = self.ds.env().epicsStore()
self.clen = self.epics.value(self.clenStr)
elif 'rayonix' in self.detInfo.lower() and 'xpp' in self.experimentName:
self.epics = self.ds.env().epicsStore()
self.clen = self.epics.value(self.clenStr)
def getDetectorAlias(self, srcOrAlias):
for i in self.detInfoList:
src, alias, _ = i
if srcOrAlias.lower() == src.lower() or srcOrAlias.lower() == alias.lower():
return alias
def getDetInfoList(self):
myAreaDetectors = []
self.detnames = psana.DetNames()
for k in self.detnames:
try:
if Detector.PyDetector.dettype(str(k[0]), self.env) == Detector.AreaDetector.AreaDetector:
myAreaDetectors.append(k)
except ValueError:
continue
self.detInfoList = list(set(myAreaDetectors))
def getEvent(self, number):
self.evt = self.run.event(self.times[number])
def getImg(self, number):
self.getEvent(number)
img = self.det.image(self.evt, self.det.calib(self.evt))
return img
def getImg(self):
if self.evt is not None:
img = self.det.image(self.evt, self.det.calib(self.evt))
return img
return None
def getCheetahImg(self, calib=None):
"""Converts seg, row, col assuming (32,185,388)
to cheetah 2-d table row and col (8*185, 4*388)
"""
if 'cspad2x2' in self.detInfo.lower():
print "Not implemented yet: cspad2x2"
elif 'cspad' in self.detInfo.lower():
if calib is None: calib = self.det.calib(self.evt) # (32,185,388)
img = np.zeros((8 * 185, 4 * 388))
counter = 0
for quad in range(4):
for seg in range(8):
img[seg * 185:(seg + 1) * 185, quad * 388:(quad + 1) * 388] = calib[counter, :, :]
counter += 1
elif 'rayonix' in self.detInfo.lower():
if calib is None:
img = np.squeeze(self.det.calib(self.evt)) # (1920,1920)
else:
img = np.squeeze(calib)
return img
def getCleanAssembledImg(self, backgroundEvent):
"""Returns psana assembled image
"""
backgroundEvt = self.run.event(self.times[backgroundEvent])
backgroundCalib = self.det.calib(backgroundEvt)
calib = self.det.calib(self.evt)
cleanCalib = calib - backgroundCalib
img = self.det.image(self.evt, cleanCalib)
return img
def getAssembledImg(self):
"""Returns psana assembled image
"""
img = self.det.image(self.evt)
return img
def getCalibImg(self):
"""Returns psana assembled image
"""
img = self.det.calib(self.evt)
return img
def getCleanAssembledPhotons(self, backgroundEvent):
"""Returns psana assembled image in photon counts
"""
backgroundEvt = self.run.event(self.times[backgroundEvent])
backgroundCalib = self.det.calib(backgroundEvt)
calib = self.det.calib(self.evt)
cleanCalib = calib - backgroundCalib
img = self.det.photons(self.evt, nda_calib=cleanCalib, adu_per_photon=self.aduPerPhoton)
phot = self.det.image(self.evt, img)
return phot
def getAssembledPhotons(self):
"""Returns psana assembled image in photon counts
"""
img = self.det.photons(self.evt, adu_per_photon=self.aduPerPhoton)
phot = self.det.image(self.evt, img)
return phot
def getPsanaEvent(self, cheetahFilename):
# Gets psana event given cheetahFilename, e.g. LCLS_2015_Jul26_r0014_035035_e820.h5
hrsMinSec = cheetahFilename.split('_')[-2]
fid = int(cheetahFilename.split('_')[-1].split('.')[0], 16)
for t in self.times:
if t.fiducial() == fid:
localtime = time.strftime('%H:%M:%S', time.localtime(t.seconds()))
localtime = localtime.replace(':', '')
if localtime[0:3] == hrsMinSec[0:3]:
self.evt = self.run.event(t)
else:
self.evt = None
def getStartTime(self):
self.evt = self.run.event(self.times[0])
evtId = self.evt.get(psana.EventId)
sec = evtId.time()[0]
nsec = evtId.time()[1]
fid = evtId.fiducials()
return time.strftime('%FT%H:%M:%S-0800', time.localtime(sec)) # Hard-coded pacific time
#####################################################################
# TODO: Functions below are not being used yet
#####################################################################
def findPsanaGeometry(self):
try:
self.source = psana.Detector.PyDetector.map_alias_to_source(self.detInfo,
self.ds.env()) # 'DetInfo(CxiDs2.0:Cspad.0)'
self.calibSource = self.source.split('(')[-1].split(')')[0] # 'CxiDs2.0:Cspad.0'
self.detectorType = gu.det_type_from_source(self.source) # 1
self.calibGroup = gu.dic_det_type_to_calib_group[self.detectorType] # 'CsPad::CalibV1'
self.detectorName = gu.dic_det_type_to_name[self.detectorType].upper() # 'CSPAD'
if self.localCalib:
self.calibPath = "./calib/" + self.calibGroup + "/" + self.calibSource + "/geometry"
else:
self.calibPath = "/reg/d/psdm/" + self.parent.experimentName[0:3] + \
"/" + self.parent.experimentName + "/calib/" + \
self.calibGroup + "/" + self.calibSource + "/geometry"
# Determine which calib file to use
geometryFiles = os.listdir(self.calibPath)
self.calibFile = None
minDiff = -1e6
for fname in geometryFiles:
if fname.endswith('.data'):
endValid = False
startNum = int(fname.split('-')[0])
endNum = fname.split('-')[-1].split('.data')[0]
diff = startNum - self.parent.runNumber
# Make sure it's end number is valid too
if 'end' in endNum:
endValid = True
else:
try:
if self.parent.runNumber <= int(endNum):
endValid = True
except:
continue
if diff <= 0 and diff > minDiff and endValid is True:
minDiff = diff
self.calibFile = fname
except:
if self.parent.args.v >= 1: print "Couldn't find psana geometry"
self.calibFile = None
def setupRadialBackground(self):
self.findPsanaGeometry()
if self.calibFile is not None:
self.geo = GeometryAccess(self.calibPath+'/'+self.calibFile)
self.xarr, self.yarr, self.zarr = self.geo.get_pixel_coords()
self.iX, self.iY = self.geo.get_pixel_coord_indexes()
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)
else:
self.rb = None
def updatePolarizationFactor(self, detectorDistance_in_m):
if self.rb is not None:
self.pf = polarization_factor(self.rb.pixel_rad(), self.rb.pixel_phi(), detectorDistance_in_m*1e6) # convert to um
def getCalib(self, evtNumber):
if self.run is not None:
self.evt = self.getEvent(evtNumber)
if self.applyCommonMode: # play with different common mode
if self.commonMode[0] == 5: # Algorithm 5
calib = self.det.calib(self.evt,cmpars=(self.commonMode[0], self.commonMode[1]))
else: # Algorithms 1 to 4
print "### Overriding common mode: ", self.commonMode
calib = self.det.calib(self.evt,cmpars=(self.commonMode[0], self.commonMode[1],
self.commonMode[2], self.commonMode[3]))
else:
calib = self.det.calib(self.evt)
return calib
else:
return None
def getPreprocessedImage(self, evtNumber, image_property):
disp_medianCorrection = 19
disp_radialCorrection = 18
disp_gainMask = 17
disp_coordy= 16
disp_coordx= 15
disp_col= 14
disp_row= 13
disp_seg= 12
disp_quad= 11
disp_gain= 10
disp_commonMode= 9
disp_rms= 8
disp_status= 7
disp_pedestal= 6
disp_photons= 5
disp_raw= 4
disp_pedestalCorrected= 3
disp_commonModeCorrected= 2
disp_adu= 1
if image_property == disp_medianCorrection: # median subtraction
print "Sorry, this feature isn't available yet"
elif image_property == disp_radialCorrection: # radial subtraction + polarization corrected
self.getEvent(evtNumber)
calib = self.getCalib(evtNumber)
if calib:
self.pf.shape = self.parent.calib.shape
calib = self.rb.subtract_bkgd(calib * self.pf)
elif image_property == disp_adu: # gain and hybrid gain corrected
calib = self.getCalib(evtNumber)
elif image_property == disp_commonModeCorrected: # common mode corrected
calib = self.getCommonModeCorrected(evtNumber)
elif image_property == disp_pedestalCorrected: # pedestal corrected
calib = self.det.raw(self.evt).astype('float32')
if calib: calib -= self.det.pedestals(self.evt)
elif image_property == disp_raw: # raw
calib = self.det.raw(self.evt)
elif image_property == disp_photons: # photon counts
calib = self.det.photons(self.evt, mask=self.parent.mk.userMask,
adu_per_photon=self.parent.exp.aduPerPhoton)
if calib is None:
calib = np.zeros_like(self.parent.exp.detGuaranteed, dtype='int32')
elif image_property == disp_pedestal: # pedestal
calib = self.parent.det.pedestals(self.parent.evt)
elif image_property == disp_status: # status
calib = self.parent.det.status(self.parent.evt)
elif image_property == disp_rms: # rms
calib = self.parent.det.rms(self.parent.evt)
elif image_property == disp_commonMode: # common mode
calib = self.getCommonMode(evtNumber)
elif image_property == disp_gain: # gain
calib = self.parent.det.gain(self.parent.evt)
elif image_property == disp_gainMask: # gain_mask
calib = self.parent.det.gain_mask(self.parent.evt)
elif image_property == disp_coordx: # coords_x
calib = self.parent.det.coords_x(self.parent.evt)
elif image_property == disp_coordy: # coords_y
calib = self.parent.det.coords_y(self.parent.evt)
shape = self.parent.det.shape(self.parent.evt)
if len(shape) == 3:
if image_property == disp_quad: # quad ind
calib = np.zeros(shape)
for i in range(shape[0]):
# FIXME: handle detectors properly
if shape[0] == 32: # cspad
calib[i, :, :] = int(i) % 8
elif shape[0] == 2: # cspad2x2
calib[i, :, :] = int(i) % 2
elif shape[0] == 4: # pnccd
calib[i, :, :] = int(i) % 4
elif image_property == disp_seg: # seg ind
calib = np.zeros(shape)
if shape[0] == 32: # cspad
for i in range(32):
calib[i, :, :] = int(i) / 8
elif shape[0] == 2: # cspad2x2
for i in range(2):
calib[i, :, :] = int(i)
elif shape[0] == 4: # pnccd
for i in range(4):
calib[i, :, :] = int(i)
elif image_property == disp_row: # row ind
calib = np.zeros(shape)
if shape[0] == 32: # cspad
for i in range(185):
calib[:, i, :] = i
elif shape[0] == 2: # cspad2x2
for i in range(185):
calib[:, i, :] = i
elif shape[0] == 4: # pnccd
for i in range(512):
calib[:, i, :] = i
elif image_property == disp_col: # col ind
calib = np.zeros(shape)
if shape[0] == 32: # cspad
for i in range(388):
calib[:, :, i] = i
elif shape[0] == 2: # cspad2x2
for i in range(388):
calib[:, :, i] = i
elif shape[0] == 4: # pnccd
for i in range(512):
calib[:, :, i] = i
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)
