def __init__(self, exp, run, detname, evt, detector, litPixelThreshold,
streakMask_on, streakMask_sigma, streakMask_width,
userMask_path, psanaMask_on, psanaMask_calib,
psanaMask_status, psanaMask_edges, psanaMask_central,
psanaMask_unbond, psanaMask_unbondnrs, **kwargs):
self.exp = exp
self.run = run
self.detname = detname
self.det = detector
self.litPixelThreshold = litPixelThreshold
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.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.StreakMask = myskbeam.StreakMask(self.det,
evt,
width=self.streakMask_width,
sigma=self.streakMask_sigma)
def initMask(self):
if self.parent.det is not None:
if self.gapAssemInd is None:
self.gapAssem = self.parent.det.image(self.parent.evt, np.ones_like(self.parent.exp.detGuaranteed,dtype='int'))
self.gapAssemInd = np.where(self.gapAssem==0)
if self.userMask is None and self.parent.data is not None:
# initialize
self.userMaskAssem = np.ones_like(self.parent.data,dtype='int')
self.userMask = self.parent.det.ndarray_from_image(self.parent.evt,self.userMaskAssem, pix_scale_size_um=None, xy0_off_pix=None)
if self.streakMask is None:
self.StreakMask = myskbeam.StreakMask(self.parent.det, self.parent.evt, width=self.streak_width, sigma=self.streak_sigma)
if self.parent.args.v >= 1: print("Done initMask")
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 __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)
