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treeVars.py
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import numpy as np
class AutoFillTreeProducer:
def __init__(self,tree, fullinfosc):
self.outTree = tree
self.scfullinfo = fullinfosc
def createPMTVariables(self):
self.outTree.branch('pmt_integral', 'F')
self.outTree.branch('pmt_tot', 'F')
self.outTree.branch('pmt_amplitude', 'F', lenVar='nPeak')
self.outTree.branch('pmt_time', 'F', lenVar='nPeak')
self.outTree.branch('pmt_prominence', 'F', lenVar='nPeak')
self.outTree.branch('pmt_fwhm', 'F', lenVar='nPeak')
self.outTree.branch('pmt_hm', 'F', lenVar='nPeak')
self.outTree.branch('pmt_risetime', 'F', lenVar='nPeak')
self.outTree.branch('pmt_falltime', 'F', lenVar='nPeak')
def fillPMTVariables(self,peakFinder,sampleSize):
self.outTree.fillBranch('pmt_integral',peakFinder.getIntegral()*sampleSize)
self.outTree.fillBranch('pmt_tot',peakFinder.getTot())
self.outTree.fillBranch('pmt_amplitude',peakFinder.getAmplitudes())
self.outTree.fillBranch('pmt_time',peakFinder.getPeakTimes())
self.outTree.fillBranch('pmt_prominence',peakFinder.getProminences())
self.outTree.fillBranch('pmt_fwhm',peakFinder.getFWHMs())
self.outTree.fillBranch('pmt_hm',peakFinder.getHMs())
self.outTree.fillBranch('pmt_risetime',peakFinder.getTimes('rise'))
self.outTree.fillBranch('pmt_falltime',peakFinder.getTimes('fall'))
def createCameraVariables(self):
self.outTree.branch('cmos_integral', 'F')
self.outTree.branch('cmos_mean', 'F')
self.outTree.branch('cmos_rms', 'F')
def createClusterVariables(self,name='track'):
chars = list(name)
start = chars[0]; rest = chars[1:]
sizeStr = 'n'+start.upper()+''.join(rest)
self.outTree.branch('{name}_size'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_nhits'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_integral'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_corrintegral'.format(name=name), 'F', lenVar=sizeStr)
# filled only for the supercluster
if name=='sc':
self.outTree.branch('{name}_nslices'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_energy'.format(name=name), 'F', lenVar=sizeStr)
##### MC truth
# self.outTree.branch('{name}_MC_pathlength'.format(name=name), 'F', lenVar=sizeStr)
# self.outTree.branch('{name}_MC_px'.format(name=name), 'F', lenVar=sizeStr)
# self.outTree.branch('{name}_MC_py'.format(name=name), 'F', lenVar=sizeStr)
# self.outTree.branch('{name}_MC_pz'.format(name=name), 'F', lenVar=sizeStr)
# self.outTree.branch('{name}_MC_x_vertex'.format(name=name), 'F', lenVar=sizeStr)
# self.outTree.branch('{name}_MC_y_vertex'.format(name=name), 'F', lenVar=sizeStr)
# self.outTree.branch('{name}_MC_z_vertex'.format(name=name), 'F', lenVar=sizeStr)
### end of mc truth
self.outTree.branch('{name}_pathlength'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_energyprof'.format(name=name), 'F', lenVar=sizeStr+'*nSlices')
self.outTree.branch('{name}_xprof'.format(name=name), 'F', lenVar=sizeStr+'*nSlices')
self.outTree.branch('{name}_yprof'.format(name=name), 'F', lenVar=sizeStr+'*nSlices')
self.outTree.branch('{name}_mindist'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_nmatchweak'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_nmatchrobust'.format(name=name), 'F', lenVar=sizeStr)
if self.scfullinfo == True:
self.outTree.branch('{name}_ID'.format(name=name), 'I', lenVar=sizeStr+'*nIntpixels')
self.outTree.branch('{name}_nintpixels'.format(name=name), 'I', lenVar=sizeStr)
self.outTree.branch('{name}_xpixelcoord'.format(name=name), 'F', lenVar=sizeStr+'*nIntpixels')
self.outTree.branch('{name}_ypixelcoord'.format(name=name), 'F', lenVar=sizeStr+'*nIntpixels')
self.outTree.branch('{name}_zpixel'.format(name=name), 'F', lenVar=sizeStr+'*nIntpixels')
self.outTree.branch('{name}_IDall'.format(name=name), 'I', lenVar=sizeStr+'*nAllintpixels')
self.outTree.branch('{name}_nallintpixels'.format(name=name), 'I', lenVar=sizeStr)
self.outTree.branch('{name}_xallpixelcoord'.format(name=name), 'F', lenVar=sizeStr+'*nAllintpixels')
self.outTree.branch('{name}_yallpixelcoord'.format(name=name), 'F', lenVar=sizeStr+'*nAllintpixels')
self.outTree.branch('{name}_zallpixel'.format(name=name), 'F', lenVar=sizeStr+'*nAllintpixels')
self.outTree.branch('{name}_theta'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_length'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_width'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_longrms'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_latrms'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_lfullrms'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_tfullrms'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_lp0amplitude'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_lp0prominence'.format(name=name),'F', lenVar=sizeStr)
self.outTree.branch('{name}_lp0fwhm'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_lp0mean'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_tp0fwhm'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_iteration'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_xmean'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_ymean'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_xmax'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_xmin'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_ymax'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_ymin'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_nclu'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_pearson'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_tgaussamp'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_tgaussmean'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_tgausssigma'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_tchi2'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_tstatus'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_lgaussamp'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_lgaussmean'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_lgausssigma'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_lchi2'.format(name=name), 'F', lenVar=sizeStr)
self.outTree.branch('{name}_lstatus'.format(name=name), 'F', lenVar=sizeStr)
def fillCameraVariables(self,pic):
self.outTree.fillBranch('cmos_integral',np.sum(pic))
self.outTree.fillBranch('cmos_mean',np.mean(pic))
self.outTree.fillBranch('cmos_rms',np.std(pic))
def fillClusterVariables(self,clusters,name='track'):
chars = list(name)
start = chars[0]; rest = chars[1:]
sizeStr = 'n'+start.upper()+''.join(rest)
self.outTree.fillBranch('{name}_size'.format(name=name), [cl.size() for cl in clusters])
self.outTree.fillBranch('{name}_nhits'.format(name=name), [cl.sizeActive() for cl in clusters])
self.outTree.fillBranch('{name}_integral'.format(name=name), [cl.integral() for cl in clusters])
self.outTree.fillBranch('{name}_corrintegral'.format(name=name), [cl.corr_integral() for cl in clusters])
# filled only for the supercluster
if name=='sc':
self.outTree.fillBranch('{name}_nslices'.format(name=name), [cl.nslices for cl in clusters])
self.outTree.fillBranch('{name}_energy'.format(name=name), [cl.calibratedEnergy for cl in clusters])
self.outTree.fillBranch('{name}_pathlength'.format(name=name), [cl.pathlength for cl in clusters])
self.outTree.fillBranch('{name}_energyprof'.format(name=name), [cl.energyprofile[i] for cl in clusters for i in range(cl.nslices)])
self.outTree.fillBranch('{name}_xprof'.format(name=name), [cl.centers[i][0] for cl in clusters for i in range(cl.nslices)])
self.outTree.fillBranch('{name}_yprof'.format(name=name), [cl.centers[i][1] for cl in clusters for i in range(cl.nslices)])
self.outTree.fillBranch('{name}_mindist'.format(name=name), [cl.minDistKiller for cl in clusters])
self.outTree.fillBranch('{name}_nmatchweak'.format(name=name), [cl.nMatchKillerWeak for cl in clusters])
self.outTree.fillBranch('{name}_nmatchrobust'.format(name=name), [cl.nMatchKiller for cl in clusters])
if self.scfullinfo == True:
self.outTree.fillBranch('{name}_ID'.format(name=name), [cl.ID[i] for cl in clusters for i in range(cl.nintpixels)])
self.outTree.fillBranch('{name}_nintpixels'.format(name=name), [cl.nintpixels for cl in clusters])
self.outTree.fillBranch('{name}_xpixelcoord'.format(name=name), [cl.xpixelcoord[i] for cl in clusters for i in range(cl.nintpixels)])
self.outTree.fillBranch('{name}_ypixelcoord'.format(name=name), [cl.ypixelcoord[i] for cl in clusters for i in range(cl.nintpixels)])
self.outTree.fillBranch('{name}_zpixel'.format(name=name), [cl.zpixel[i] for cl in clusters for i in range(cl.nintpixels)])
self.outTree.fillBranch('{name}_IDall'.format(name=name), [cl.IDall[i] for cl in clusters for i in range(cl.nallintpixels)])
self.outTree.fillBranch('{name}_nallintpixels'.format(name=name), [cl.nallintpixels for cl in clusters])
self.outTree.fillBranch('{name}_xallpixelcoord'.format(name=name), [cl.xallpixelcoord[i] for cl in clusters for i in range(cl.nallintpixels)])
self.outTree.fillBranch('{name}_yallpixelcoord'.format(name=name), [cl.yallpixelcoord[i] for cl in clusters for i in range(cl.nallintpixels)])
self.outTree.fillBranch('{name}_zallpixel'.format(name=name), [cl.zallpixel[i] for cl in clusters for i in range(cl.nallintpixels)])
self.outTree.fillBranch('{name}_theta'.format(name=name), [cl.shapes['theta'] for cl in clusters])
self.outTree.fillBranch('{name}_length'.format(name=name), [cl.shapes['long_width'] for cl in clusters])
self.outTree.fillBranch('{name}_width'.format(name=name), [cl.shapes['lat_width'] for cl in clusters])
self.outTree.fillBranch('{name}_longrms'.format(name=name), [cl.shapes['longrms'] for cl in clusters])
self.outTree.fillBranch('{name}_latrms'.format(name=name), [cl.shapes['latrms'] for cl in clusters])
self.outTree.fillBranch('{name}_lfullrms'.format(name=name), [cl.shapes['long_fullrms'] for cl in clusters])
self.outTree.fillBranch('{name}_tfullrms'.format(name=name), [cl.shapes['lat_fullrms'] for cl in clusters])
self.outTree.fillBranch('{name}_lp0amplitude'.format(name=name), [cl.shapes['long_p0amplitude'] for cl in clusters])
self.outTree.fillBranch('{name}_lp0prominence'.format(name=name), [cl.shapes['long_p0prominence'] for cl in clusters])
self.outTree.fillBranch('{name}_lp0fwhm'.format(name=name), [cl.shapes['long_p0fwhm'] for cl in clusters])
self.outTree.fillBranch('{name}_lp0mean'.format(name=name), [cl.shapes['long_p0mean'] for cl in clusters])
self.outTree.fillBranch('{name}_tp0fwhm'.format(name=name), [cl.shapes['lat_p0fwhm'] for cl in clusters])
self.outTree.fillBranch('{name}_iteration'.format(name=name), [cl.iterations() for cl in clusters])
self.outTree.fillBranch('{name}_xmean'.format(name=name), [cl.shapes['xmean'] for cl in clusters])
self.outTree.fillBranch('{name}_ymean'.format(name=name), [cl.shapes['ymean'] for cl in clusters])
self.outTree.fillBranch('{name}_xmax'.format(name=name), [cl.shapes['xmax'] for cl in clusters])
self.outTree.fillBranch('{name}_xmin'.format(name=name), [cl.shapes['xmin'] for cl in clusters])
self.outTree.fillBranch('{name}_ymax'.format(name=name), [cl.shapes['ymax'] for cl in clusters])
self.outTree.fillBranch('{name}_ymin'.format(name=name), [cl.shapes['ymin'] for cl in clusters])
self.outTree.fillBranch('{name}_nclu'.format(name=name), [cl.getNclu() for cl in clusters])
self.outTree.fillBranch('{name}_pearson'.format(name=name), [cl.getPearson() for cl in clusters])
self.outTree.fillBranch('{name}_tgaussamp'.format(name=name), [cl.shapes['tgaussamp'] for cl in clusters])
self.outTree.fillBranch('{name}_tgaussmean'.format(name=name), [cl.shapes['tgaussmean'] for cl in clusters])
self.outTree.fillBranch('{name}_tgausssigma'.format(name=name), [cl.shapes['tgausssigma'] for cl in clusters])
self.outTree.fillBranch('{name}_tchi2'.format(name=name), [cl.shapes['tchi2'] for cl in clusters])
self.outTree.fillBranch('{name}_tstatus'.format(name=name), [cl.shapes['tstatus'] for cl in clusters])
self.outTree.fillBranch('{name}_lgaussamp'.format(name=name), [cl.shapes['lgaussamp'] for cl in clusters])
self.outTree.fillBranch('{name}_lgaussmean'.format(name=name), [cl.shapes['lgaussmean'] for cl in clusters])
self.outTree.fillBranch('{name}_lgausssigma'.format(name=name), [cl.shapes['lgausssigma'] for cl in clusters])
self.outTree.fillBranch('{name}_lchi2'.format(name=name), [cl.shapes['lchi2'] for cl in clusters])
self.outTree.fillBranch('{name}_lstatus'.format(name=name), [cl.shapes['lstatus'] for cl in clusters])