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)