def beginJob(self, outfname):
# prepare output file
self.outputFile = ROOT.TFile.Open(outfname, "RECREATE")
# prepare output tree
self.outputTree = ROOT.TTree(
"Events", "Tree containing reconstructed quantities")
self.outTree = OutputTree(self.outputFile, self.outputTree)
self.autotree = AutoFillTreeProducer(self.outTree,
self.options.scfullinfo)
self.outTree.branch("run", "I")
self.outTree.branch("event", "I")
self.outTree.branch("pedestal_run", "I")
if self.options.Save_MC_data:
self.outTree.branch("MC_track_len", "F")
self.outTree.branch("MC_px", "F")
self.outTree.branch("MC_py", "F")
self.outTree.branch("MC_pz", "F")
self.outTree.branch("MC_x_vertex", "F")
self.outTree.branch("MC_y_vertex", "F")
self.outTree.branch("MC_z_vertex", "F")
self.outTree.branch("MC_x_vertex_end", "F")
self.outTree.branch("MC_y_vertex_end", "F")
self.outTree.branch("MC_z_vertex_end", "F")
self.outTree.branch("MC_3D_pathlength", "F")
self.outTree.branch("MC_2D_pathlength", "F")
if self.options.camera_mode:
self.autotree.createCameraVariables()
self.autotree.createClusterVariables('cl')
self.autotree.createClusterVariables('sc')
if self.options.pmt_mode:
self.autotree.createPMTVariables()
def beginJob(self,outfname):
# prepare output file
self.outputFile = ROOT.TFile.Open(outfname, "RECREATE")
# prepare output tree
self.outputTree = ROOT.TTree("Events","Tree containing reconstructed quantities")
self.outTree = OutputTree(self.outputFile,self.outputTree)
self.autotree = AutoFillTreeProducer(self.outTree)
self.outTree.branch("run", "I")
self.outTree.branch("event", "I")
if self.options.camera_mode:
self.autotree.createCameraVariables()
self.autotree.createClusterVariables('cl')
self.autotree.createClusterVariables('sc')
if self.options.pmt_mode:
self.autotree.createPMTVariables()
class analysis:
def __init__(self, options):
self.xmax = 2048
self.rebin = options.rebin
self.options = options
self.pedfile_fullres_name = options.pedfile_fullres_name
self.tmpname = options.tmpname
if not os.path.exists(self.pedfile_fullres_name):
print("WARNING: pedestal file with full resolution ",
self.pedfile_fullres_name,
" not existing. First calculate them...")
self.calcPedestal(options, 1)
if not options.justPedestal:
print("Pulling pedestals...")
# first the one for clustering with rebin
ctools = cameraTools()
# then the full resolution one
pedrf_fr = ROOT.TFile.Open(self.pedfile_fullres_name)
self.pedmap_fr = pedrf_fr.Get('pedmap').Clone()
self.pedmap_fr.SetDirectory(0)
self.pedarr_fr = hist2array(self.pedmap_fr)
self.noisearr_fr = ctools.noisearray(self.pedmap_fr)
pedrf_fr.Close()
# the following is needed for multithreading
def __call__(self, evrange=(-1, -1, -1)):
if evrange[0] == -1:
outfname = self.options.outFile
else:
outfname = '{base}_chunk{ij}.root'.format(
base=self.options.outFile.split('.')[0], ij=evrange[0])
self.beginJob(outfname)
self.reconstruct(evrange)
self.endJob()
def beginJob(self, outfname):
# prepare output file
self.outputFile = ROOT.TFile.Open(outfname, "RECREATE")
# prepare output tree
self.outputTree = ROOT.TTree(
"Events", "Tree containing reconstructed quantities")
self.outTree = OutputTree(self.outputFile, self.outputTree)
self.autotree = AutoFillTreeProducer(self.outTree)
self.outTree.branch("run", "I")
self.outTree.branch("event", "I")
self.outTree.branch("pedestal_run", "I")
if self.options.camera_mode:
self.autotree.createCameraVariables()
self.autotree.createClusterVariables('cl')
self.autotree.createClusterVariables('sc')
if self.options.pmt_mode:
self.autotree.createPMTVariables()
def endJob(self):
self.outTree.write()
self.outputFile.Close()
def getNEvents(self):
tf = sw.swift_read_root_file(
self.tmpname) #tf = ROOT.TFile.Open(self.rfile)
ret = len(tf.GetListOfKeys()) if self.options.daq != 'midas' else int(
len(tf.GetListOfKeys()) / 2)
tf.Close()
return ret
def calcPedestal(self, options, alternativeRebin=-1):
maxImages = options.maxEntries
nx = ny = self.xmax
rebin = self.rebin if alternativeRebin < 0 else alternativeRebin
nx = int(nx / rebin)
ny = int(ny / rebin)
#pedfilename = 'pedestals/pedmap_ex%d_rebin%d.root' % (options.pedexposure,rebin)
pedfilename = 'pedestals/pedmap_run%s_rebin%d.root' % (options.run,
rebin)
pedfile = ROOT.TFile.Open(pedfilename, 'recreate')
pedmap = ROOT.TH2D('pedmap', 'pedmap', nx, 0, self.xmax, ny, 0,
self.xmax)
pedmapS = ROOT.TH2D('pedmapsigma', 'pedmapsigma', nx, 0, self.xmax, ny,
0, self.xmax)
pedsum = np.zeros((nx, ny))
tf = sw.swift_read_root_file(self.tmpname)
#tf = ROOT.TFile.Open(self.rfile)
# first calculate the mean
numev = 0
for i, e in enumerate(tf.GetListOfKeys()):
iev = i if self.options.daq != 'midas' else i / 2 # when PMT is present
if iev in self.options.excImages: continue
if maxImages > -1 and i < len(tf.GetListOfKeys()) - maxImages:
continue
name = e.GetName()
obj = e.ReadObj()
if not obj.InheritsFrom('TH2'): continue
print("Calc pedestal mean with event: ", name)
if rebin > 1:
obj.RebinX(rebin)
obj.RebinY(rebin)
arr = hist2array(obj)
pedsum = np.add(pedsum, arr)
numev += 1
pedmean = pedsum / float(numev)
# now compute the rms (two separate loops is faster than one, yes)
pedsqdiff = np.zeros((nx, ny))
for i, e in enumerate(tf.GetListOfKeys()):
iev = i if self.options.daq != 'midas' else i / 2 # when PMT is present
if iev in self.options.excImages: continue
if maxImages > -1 and i < len(tf.GetListOfKeys()) - maxImages:
continue
name = e.GetName()
obj = e.ReadObj()
if not obj.InheritsFrom('TH2'): continue
print("Calc pedestal rms with event: ", name)
if rebin > 1:
obj.RebinX(rebin)
obj.RebinY(rebin)
arr = hist2array(obj)
pedsqdiff = np.add(pedsqdiff, np.square(np.add(arr, -1 * pedmean)))
pedrms = np.sqrt(pedsqdiff / float(numev - 1))
# now save in a persistent ROOT object
for ix in range(nx):
for iy in range(ny):
pedmap.SetBinContent(ix + 1, iy + 1, pedmean[ix, iy])
pedmap.SetBinError(ix + 1, iy + 1, pedrms[ix, iy])
pedmapS.SetBinContent(ix + 1, iy + 1, pedrms[ix, iy])
tf.Close()
pedfile.cd()
pedmap.Write()
pedmapS.Write()
pedmean1D = ROOT.TH1D('pedmean', 'pedestal mean', 500, 97, 103)
pedrms1D = ROOT.TH1D('pedrms', 'pedestal RMS', 500, 0, 5)
for ix in range(nx):
for iy in range(ny):
pedmean1D.Fill(pedmap.GetBinContent(ix, iy))
pedrms1D.Fill(pedmap.GetBinError(ix, iy))
pedmean1D.Write()
pedrms1D.Write()
pedfile.Close()
print("Pedestal calculated and saved into ", pedfilename)
def reconstruct(self, evrange=(-1, -1, -1)):
ROOT.gROOT.Macro('rootlogon.C')
ROOT.gStyle.SetOptStat(0)
ROOT.gStyle.SetPalette(ROOT.kRainBow)
savErrorLevel = ROOT.gErrorIgnoreLevel
ROOT.gErrorIgnoreLevel = ROOT.kWarning
tf = sw.swift_read_root_file(self.tmpname)
#tf = ROOT.TFile.Open(self.rfile)
#c1 = ROOT.TCanvas('c1','',600,600)
ctools = cameraTools()
print("Reconstructing event range: ", evrange[1], "-", evrange[2])
# loop over events (pictures)
for iobj, key in enumerate(tf.GetListOfKeys()):
iev = iobj if self.options.daq != 'midas' else int(
iobj / 2) # when PMT is present
#print("max entries = ",self.options.maxEntries)
if self.options.maxEntries > 0 and iev == max(
evrange[0], 0) + self.options.maxEntries:
break
if sum(evrange[1:]) > -2:
if iev < evrange[1] or iev > evrange[2]: continue
name = key.GetName()
obj = key.ReadObj()
# Routine to skip some images if needed
if iev in self.options.excImages: continue
if self.options.debug_mode == 1 and iev != self.options.ev:
continue
if obj.InheritsFrom('TH2'):
if self.options.daq == 'btf':
run, event = (int(
name.split('_')[0].split('run')[-1].lstrip("0")),
int(name.split('_')[-1].lstrip("0")))
elif self.options.daq == 'h5':
run, event = (int(name.split('_')[0].split('run')[-1]),
int(name.split('_')[-1]))
else:
run, event = (int(
name.split('_')[1].split('run')[-1].lstrip("0")),
int(name.split('_')[-1].split('ev')[-1]))
print("Processing Run: ", run, "- Event ", event, "...")
self.outTree.fillBranch("run", run)
self.outTree.fillBranch("event", event)
self.outTree.fillBranch("pedestal_run",
int(self.options.pedrun))
if self.options.camera_mode:
if obj.InheritsFrom('TH2'):
pic_fullres = obj.Clone(obj.GetName() + '_fr')
img_fr = hist2array(pic_fullres)
# Upper Threshold full image
img_cimax = np.where(img_fr < self.options.cimax, img_fr,
0)
# zs on full image + saturation correction on full image
if self.options.saturation_corr:
#print("you are in saturation correction mode")
img_fr_sub = ctools.pedsub(img_cimax, self.pedarr_fr)
img_fr_satcor = ctools.satur_corr(img_fr_sub)
img_fr_zs = ctools.zsfullres(
img_fr_satcor,
self.noisearr_fr,
nsigma=self.options.nsigma)
img_rb_zs = ctools.arrrebin(img_fr_zs, self.rebin)
# skip saturation and set satcor =img_fr_sub
else:
#print("you are in poor mode")
img_fr_sub = ctools.pedsub(img_cimax, self.pedarr_fr)
img_fr_satcor = img_fr_sub
img_fr_zs = ctools.zsfullres(
img_fr_satcor,
self.noisearr_fr,
nsigma=self.options.nsigma)
img_rb_zs = ctools.arrrebin(img_fr_zs, self.rebin)
# Cluster reconstruction on 2D picture
algo = 'DBSCAN'
if self.options.type in ['beam', 'cosmics']: algo = 'HOUGH'
snprod_inputs = {
'picture': img_rb_zs,
'pictureHD': img_fr_satcor,
'picturezsHD': img_fr_zs,
'pictureOri': img_fr,
'name': name,
'algo': algo
}
plotpy = options.jobs < 2 # for some reason on macOS this crashes in multicore
snprod_params = {
'snake_qual': 3,
'plot2D': False,
'plotpy': False,
'plotprofiles': False
}
snprod = SnakesProducer(snprod_inputs, snprod_params,
self.options)
clusters, snakes = snprod.run()
self.autotree.fillCameraVariables(img_fr_zs)
self.autotree.fillClusterVariables(snakes, 'sc')
self.autotree.fillClusterVariables(clusters, 'cl')
if self.options.pmt_mode:
if obj.InheritsFrom('TGraph'):
# PMT waveform reconstruction
from waveform import PeakFinder, PeaksProducer
wform = tf.Get('wfm_' + '_'.join(name.split('_')[1:]))
# sampling was 5 GHz (5/ns). Rebin by 5 (1/ns)
pkprod_inputs = {'waveform': wform}
pkprod_params = {
'threshold':
options.
threshold, # min threshold for a signal (baseline is -20 mV)
'minPeakDistance':
options.
minPeakDistance, # number of samples (1 sample = 1ns )
'prominence':
options.
prominence, # noise after resampling very small
'width':
options.width, # minimal width of the signal
'resample':
options.resample, # to sample waveform at 1 GHz only
'rangex': (self.options.time_range[0],
self.options.time_range[1]),
'plotpy':
options.pmt_plotpy
}
pkprod = PeaksProducer(pkprod_inputs, pkprod_params,
self.options)
peaksfinder = pkprod.run()
self.autotree.fillPMTVariables(
peaksfinder, 0.2 * pkprod_params['resample'])
# fill reco tree (just once/event, and the TGraph is analyses as last)
if (self.options.daq == 'midas' and
obj.InheritsFrom('TGraph')) or self.options.daq != 'midas':
self.outTree.fill()
ROOT.gErrorIgnoreLevel = savErrorLevel