pileupCalc.py

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#!/usr/bin/env python
VERSION='1.00'
import os,sys,time
import optparse
from RecoLuminosity.LumiDB import pileupParser
from RecoLuminosity.LumiDB import selectionParser
from math import exp
from math import sqrt
import six

def parseInputFile(inputfilename):
    '''
    output ({run:[ls:[inlumi, meanint]]})
    '''
    selectf=open(inputfilename,'r')
    inputfilecontent=selectf.read()
    p=pileupParser.pileupParser(inputfilecontent)                            
    
#    p=inputFilesetParser.inputFilesetParser(inputfilename)
    runlsbyfile=p.runsandls()
    return runlsbyfile

def MyErf(input):

    # Abramowitz and Stegun approximations for Erf (equations 7.1.25-28)
    X = abs(input)

    p = 0.47047
    b1 = 0.3480242
    b2 = -0.0958798
    b3 = 0.7478556

    T = 1.0/(1.0+p*X)
    cErf = 1.0 - (b1*T + b2*T*T + b3*T*T*T)*exp(-1.0*X*X)
    if input<0:
        cErf = -1.0*cErf

    # Alternate Erf approximation:
    
    #A1 = 0.278393
    #A2 = 0.230389
    #A3 = 0.000972
    #A4 = 0.078108

    #term = 1.0+ A1*X+ A2*X*X+ A3*X*X*X+ A4*X*X*X*X
    #denom = term*term*term*term

    #dErf = 1.0 - 1.0/denom
    #if input<0:
    #    dErf = -1.0*dErf
        
    return cErf


def fillPileupHistogram (lumiInfo, calcOption, hist, minbXsec, Nbins):
    '''
    lumiinfo:[intlumi per LS, mean interactions ]

    intlumi is the deadtime corrected average integraged lumi per lumisection
    '''

    LSintLumi = lumiInfo[0]
    RMSInt = lumiInfo[1]*minbXsec
    AveNumInt = lumiInfo[2]*minbXsec

    #coeff = 0

    #if RMSInt > 0:
    #    coeff = 1.0/RMSInt/sqrt(6.283185)

    #expon = 2.0*RMSInt*RMSInt

    Sqrt2 = sqrt(2)

    ##Nbins = hist.GetXaxis().GetNbins()

    ProbFromRMS = []
    BinWidth = hist.GetBinWidth(1)

    # First, re-constitute lumi distribution for this LS from RMS:
    if RMSInt > 0:

        AreaLnew = -10.
        AreaL = 0

        for obs in range (Nbins):
            #Old Gaussian normalization; inaccurate for small rms and large bins
            #val = hist.GetBinCenter(obs+1)
            #prob = coeff*exp(-1.0*(val-AveNumInt)*(val-AveNumInt)/expon)
            #ProbFromRMS.append(prob)
            
            left = hist.GetBinLowEdge(obs+1)
            right = left+BinWidth

            argR = (AveNumInt-right)/Sqrt2/RMSInt
            AreaR = MyErf(argR)

            if AreaLnew<-5.:
                argL = (AveNumInt-left)/Sqrt2/RMSInt
                AreaL = MyErf(argL)
            else:
                AreaL = AreaLnew
                AreaLnew = AreaR  # save R bin value for L next time

            NewProb = (AreaL-AreaR)*0.5

            ProbFromRMS.append(NewProb)

            #print left, right, argL, argR, AreaL, AreaR, NewProb

    else:
        obs = hist.FindBin(AveNumInt)
        for bin in range (Nbins):
            ProbFromRMS.append(0.0)
        if obs<Nbins+1:            
            ProbFromRMS[obs] = 1.0
        if AveNumInt < 1.0E-5:
           ProbFromRMS[obs] = 0.  # just ignore zero values
        
    if calcOption == 'true':  # Just put distribution into histogram
        if RMSInt > 0:
            totalProb = 0
            for obs in range (Nbins):
                prob = ProbFromRMS[obs]
                val = hist.GetBinCenter(obs+1)
                #print obs, val, RMSInt,coeff,expon,prob
                totalProb += prob
                hist.Fill (val, prob * LSintLumi)
                
            if 1.0-totalProb > 0.01:
                print "Significant probability density outside of your histogram"
                print "Consider using a higher value of --maxPileupBin"
                print "Mean %f, RMS %f, Integrated probability %f" % (AveNumInt,RMSInt,totalProb)
            #    hist.Fill (val, (1 - totalProb) * LSintLumi)
        else:
            hist.Fill(AveNumInt,LSintLumi)
    else: # have to convolute with a poisson distribution to get observed Nint
        totalProb = 0
        Peak = 0
        BinWidth = hist.GetBinWidth(1)
        for obs in range (Nbins):
            Peak = hist.GetBinCenter(obs+1)
            RMSWeight = ProbFromRMS[obs]
            for bin in range (Nbins):
                val = hist.GetBinCenter(bin+1)-0.5*BinWidth
                prob = ROOT.TMath.Poisson (val, Peak)
                totalProb += prob
                hist.Fill (val, prob * LSintLumi * RMSWeight)

        if 1.0-totalProb > 0.01:
            print "Significant probability density outside of your histogram"
            print "Consider using a higher value of --maxPileupBin"


    return hist



##############################
## ######################## ##
## ## ################## ## ##
## ## ## Main Program ## ## ##
## ## ################## ## ##
## ######################## ##
##############################

if __name__ == '__main__':

    parser = optparse.OptionParser ("Usage: %prog [--options] output.root",
                                    description = "Script to estimate pileup distribution using xing instantaneous luminosity information and minimum bias cross section.  Output is TH1D stored in root file")
#
#    parser = argparse.ArgumentParser(prog=os.path.basename(sys.argv[0]),description = "Pileup Lumi Calculation",formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    CalculationModeChoices = ['truth', 'observed']

    #
    # parse arguments
    #  
    #
    # basic arguments
    #
    #parser.add_argument('action',choices=allowedActions,
    #                    help='command actions')
    parser.add_option('-o',dest='outputfile',action='store',
                        default='PileupCalc.root',
                        help='output root file')
    parser.add_option('-i',dest='inputfile',action='store',
                        help='Input Run/LS file for your analysis in JSON format (required)')
    parser.add_option('--inputLumiJSON',dest='inputLumiJSON',action='store',
                        help='Input Lumi/Pileup file in JSON format (required)')
    parser.add_option('--calcMode',dest='calcMode',action='store',
                        help='Calculate either True ="true" or Observed="observed" distributions')
    parser.add_option('--minBiasXsec',dest='minBiasXsec',action='store',
                        type=float,
                        default=73500,
                        help='Minimum bias cross section assumed (in microbbarn), default %default microbarn')
    parser.add_option('--maxPileupBin',dest='maxPileupBin',action='store',
                        type=int,
                        default=25,
                        help='Maximum value of pileup histogram, default %default')
    parser.add_option('--numPileupBins',dest='numPileupBins',action='store',
                        type=int,
                        default=1000,
                        help='number of bins in pileup histogram, default %default')
    parser.add_option('--pileupHistName',dest='pileupHistName',action='store',
                        default='pileup',
                        help='name of pileup histogram, default %default')
    parser.add_option('--verbose',dest='verbose',action='store_true',help='verbose mode for printing' )
    
    # parse arguments
    try:
        (options, args) = parser.parse_args()
    except Exception as e:
        print e
    if not args:
        parser.print_usage()
        sys.exit()
    if len (args) != 1:
        parser.print_usage()
        raise RuntimeError("Exactly one output file must be given")
    output = args[0]
    
#    options=parser.parse_args()

    if options.verbose:
        print 'General configuration'
        print '\toutputfile: ',options.outputfile
        print '\tAction: ',options.calcMode, 'luminosity distribution will be calculated'
        print '\tinput selection file: ',options.inputfile
        print '\tMinBiasXsec: ',options.minBiasXsec
        print '\tmaxPileupBin: ',options.maxPileupBin
        print '\tnumPileupBins: ',options.numPileupBins

    import ROOT 
    pileupHist = ROOT.TH1D (options.pileupHistName,options.pileupHistName,
                      options.numPileupBins,
                      0., options.maxPileupBin)

    nbins = options.numPileupBins
    upper = options.maxPileupBin

    inpf = open (options.inputfile, 'r')
    inputfilecontent = inpf.read()
    inputRange =  selectionParser.selectionParser (inputfilecontent).runsandls()

    #inputRange=inputFilesetParser.inputFilesetParser(options.inputfile)

    if options.calcMode in ['true','observed']:
        inputPileupRange=parseInputFile(options.inputLumiJSON)

        # now, we have to find the information for the input runs and LumiSections 
        # in the Lumi/Pileup list. First, loop over inputs

        for (run, lslist) in sorted (six.iteritems(inputRange)) ):
            # now, look for matching run, then match lumi sections
            # print "searching for run %d" % (run)
            if run in inputPileupRange.keys():
                #print run
                LSPUlist = inputPileupRange[run]
                # print "LSPUlist", LSPUlist
                for LSnumber in lslist:
                    if LSnumber in LSPUlist.keys():
                        #print "found LS %d" % (LSnumber)
                        lumiInfo = LSPUlist[LSnumber]
                        # print lumiInfo
                        fillPileupHistogram (lumiInfo, options.calcMode,
                                pileupHist, options.minBiasXsec, nbins)
                    else: # trouble
                        print "Run %d, LumiSection %d not found in Lumi/Pileup input file. Check your files!" \
                                % (run,LSnumber)

            else:  # trouble
                print "Run %d not found in Lumi/Pileup input file.  Check your files!" % (run)



#            print run
#            print lslist

        histFile = ROOT.TFile.Open (output, 'recreate')
        if not histFile:
            raise RuntimeError("Could not open '%s' as an output root file" % output)
        pileupHist.Write()
        #for hist in histList:
        #    hist.Write()
        histFile.Close()
        sys.exit()

    else:
        print "must specify a pileup calculation mode via --calcMode true or --calcMode observed"
        sys.exit()