| 1 |
bortigno |
1.1 |
#!/usr/bin/env python
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import re
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from sys import argv, stdout, stderr, exit
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from optparse import OptionParser
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# import ROOT with a fix to get batch mode (http://root.cern.ch/phpBB3/viewtopic.php?t=3198)
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hasHelp = False
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for X in ("-h", "-?", "--help"):
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if X in argv:
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hasHelp = True
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argv.remove(X)
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argv.append( '-b-' )
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import ROOT
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ROOT.gROOT.SetBatch(True)
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ROOT.gSystem.Load("libHiggsAnalysisCombinedLimit.so")
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argv.remove( '-b-' )
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if hasHelp: argv.append("-h")
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parser = OptionParser(usage="usage: %prog [options] in.root \nrun with --help to get list of options")
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parser.add_option("--vtol", "--val-tolerance", dest="vtol", default=0.30, type="float", help="Report nuisances whose value changes by more than this amount of sigmas")
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parser.add_option("--stol", "--sig-tolerance", dest="stol", default=0.10, type="float", help="Report nuisances whose sigma changes by more than this amount")
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parser.add_option("--vtol2", "--val-tolerance2", dest="vtol2", default=2.0, type="float", help="Report severely nuisances whose value changes by more than this amount of sigmas")
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parser.add_option("--stol2", "--sig-tolerance2", dest="stol2", default=0.50, type="float", help="Report severely nuisances whose sigma changes by more than this amount")
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parser.add_option("-a", "--all", dest="all", default=False, action="store_true", help="Print all nuisances, even the ones which are unchanged w.r.t. pre-fit values.")
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parser.add_option("-A", "--absolute", dest="abs", default=False, action="store_true", help="Report also absolute values of nuisance values and errors, not only the ones normalized to the input sigma")
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parser.add_option("-p", "--poi", dest="poi", default="r", type="string", help="Name of signal strength parameter (default is 'r' as per text2workspace.py)")
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parser.add_option("-f", "--format", dest="format", default="text", type="string", help="Output format ('text', 'latex', 'twiki'")
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parser.add_option("-g", "--histogram", dest="plotfile", default=None, type="string", help="If true, plot the pulls of the nuisances to the given file.")
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parser.add_option("--sf", "--scale-factor", dest="plotsf", default=None, type="string", help="If true, plot the scale factors in a histograms.")
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(options, args) = parser.parse_args()
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if len(args) == 0:
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parser.print_usage()
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exit(1)
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file = ROOT.TFile(args[0])
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if file == None: raise RuntimeError, "Cannot open file %s" % args[0]
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fit_s = file.Get("fit_s")
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fit_b = file.Get("fit_b")
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prefit = file.Get("nuisances_prefit")
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if fit_s == None or fit_s.ClassName() != "RooFitResult": raise RuntimeError, "File %s does not contain the output of the signal fit 'fit_s'" % args[0]
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if fit_b == None or fit_b.ClassName() != "RooFitResult": raise RuntimeError, "File %s does not contain the output of the background fit 'fit_b'" % args[0]
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if prefit == None or prefit.ClassName() != "RooArgSet": raise RuntimeError, "File %s does not contain the prefit nuisances 'nuisances_prefit'" % args[0]
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isFlagged = {}
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table = {}
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fpf_b = fit_b.floatParsFinal()
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fpf_s = fit_s.floatParsFinal()
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pulls = []
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for i in range(fpf_s.getSize()):
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nuis_s = fpf_s.at(i)
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name = nuis_s.GetName();
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nuis_b = fpf_b.find(name)
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nuis_p = prefit.find(name)
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row = []
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flag = False;
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mean_p, sigma_p = 0,0
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if nuis_p == None:
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if not options.abs: continue
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row += [ "[%.2f, %.2f]" % (nuis_s.getMin(), nuis_s.getMax()) ]
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else:
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mean_p, sigma_p = (nuis_p.getVal(), nuis_p.getError())
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if options.abs: row += [ "%.2f +/- %.2f" % (nuis_p.getVal(), nuis_p.getError()) ]
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for fit_name, nuis_x in [('b', nuis_b), ('s',nuis_s)]:
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if nuis_x == None:
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row += [ " n/a " ]
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else:
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row += [ "%+.2f +/- %.2f" % (nuis_x.getVal(), nuis_x.getError()) ]
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if nuis_p != None:
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valShift = (nuis_x.getVal() - mean_p)/sigma_p
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if fit_name == 'b':
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pulls.append(valShift)
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sigShift = nuis_x.getError()/sigma_p
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if options.abs:
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row[-1] += " (%+4.2fsig, %4.2f)" % (valShift, sigShift)
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else:
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row[-1] = " %+4.2f, %4.2f" % (valShift, sigShift)
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if (abs(valShift) > options.vtol2 or abs(sigShift-1) > options.stol2):
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isFlagged[(name,fit_name)] = 2
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flag = True
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elif (abs(valShift) > options.vtol or abs(sigShift-1) > options.stol):
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if options.all: isFlagged[(name,fit_name)] = 1
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flag = True
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elif options.all:
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flag = True
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row += [ "%+4.2f" % fit_s.correlation(name, options.poi) ]
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if flag or options.all: table[name] = row
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fmtstring = "%-40s %15s %15s %10s"
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highlight = "*%s*"
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morelight = "!%s!"
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pmsub, sigsub = None, None
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if options.format == 'text':
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if options.abs:
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fmtstring = "%-40s %15s %30s %30s %10s"
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print fmtstring % ('name', 'pre fit', 'b-only fit', 's+b fit', 'rho')
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else:
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print fmtstring % ('name', 'b-only fit', 's+b fit', 'rho')
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elif options.format == 'latex':
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pmsub = (r"(\S+) \+/- (\S+)", r"$\1 \\pm \2$")
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sigsub = ("sig", r"$\\sigma$")
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highlight = "\\textbf{%s}"
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morelight = "{{\\color{red}\\textbf{%s}}}"
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if options.abs:
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fmtstring = "%-40s & %15s & %30s & %30s & %6s \\\\"
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print "\\begin{tabular}{|l|r|r|r|r|} \\hline ";
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print (fmtstring % ('name', 'pre fit', '$b$-only fit', '$s+b$ fit', r'$\rho(\theta, \mu)$')), " \\hline"
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else:
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fmtstring = "%-40s & %15s & %15s & %6s \\\\"
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print "\\begin{tabular}{|l|r|r|r|} \\hline ";
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#what = r"$(x_\text{out} - x_\text{in})/\sigma_{\text{in}}$, $\sigma_{\text{out}}/\sigma_{\text{in}}$"
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what = r"\Delta x/\sigma_{\text{in}}$, $\sigma_{\text{out}}/\sigma_{\text{in}}$"
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print fmtstring % ('', '$b$-only fit', '$s+b$ fit', '')
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print (fmtstring % ('name', what, what, r'$\rho(\theta, \mu)$')), " \\hline"
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elif options.format == 'twiki':
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pmsub = (r"(\S+) \+/- (\S+)", r"\1 ± \2")
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sigsub = ("sig", r"σ")
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highlight = "<b>%s</b>"
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morelight = "<b style='color:red;'>%s</b>"
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if options.abs:
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fmtstring = "| <verbatim>%-40s</verbatim> | %-15s | %-30s | %-30s | %-15s |"
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print "| *name* | *pre fit* | *b-only fit* | *s+b fit* | "
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else:
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fmtstring = "| <verbatim>%-40s</verbatim> | %-15s | %-15s | %-15s |"
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print "| *name* | *b-only fit* | *s+b fit* | *corr.* |"
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elif options.format == 'html':
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pmsub = (r"(\S+) \+/- (\S+)", r"\1 ± \2")
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sigsub = ("sig", r"σ")
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highlight = "<b>%s</b>"
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morelight = "<strong>%s</strong>"
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print """
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<html><head><title>Comparison of nuisances</title>
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<style type="text/css">
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td, th { border-bottom: 1px solid black; padding: 1px 1em; }
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td { font-family: 'Consolas', 'Courier New', courier, monospace; }
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strong { color: red; font-weight: bolder; }
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</style>
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</head><body style="font-family: 'Verdana', sans-serif; font-size: 10pt;"><h1>Comparison of nuisances</h1>
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<table>
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"""
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if options.abs:
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print "<tr><th>nuisance</th><th>pre fit</th><th>background fit </th><th>signal fit</th><th>correlation</th></tr>"
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fmtstring = "<tr><td><tt>%-40s</tt> </td><td> %-15s </td><td> %-30s </td><td> %-30s </td><td> %-15s </td></tr>"
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else:
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what = "Δx/σ<sub>in</sub>, σ<sub>out</sub>/σ<sub>in</sub>";
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print "<tr><th>nuisance</th><th>background fit<br/>%s </th><th>signal fit<br/>%s</th><th>ρ(μ, θ)</tr>" % (what,what)
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fmtstring = "<tr><td><tt>%-40s</tt> </td><td> %-15s </td><td> %-15s </td><td> %-15s </td></tr>"
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names = table.keys()
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names.sort()
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highlighters = { 1:highlight, 2:morelight };
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for n in names:
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v = table[n]
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if options.format == "latex": n = n.replace(r"_", r"\_")
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if pmsub != None: v = [ re.sub(pmsub[0], pmsub[1], i) for i in v ]
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if sigsub != None: v = [ re.sub(sigsub[0], sigsub[1], i) for i in v ]
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if (n,'b') in isFlagged: v[-3] = highlighters[isFlagged[(n,'b')]] % v[-3]
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if (n,'s') in isFlagged: v[-2] = highlighters[isFlagged[(n,'s')]] % v[-2]
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if options.abs:
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print fmtstring % (n, v[0], v[1], v[2], v[3])
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else:
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print fmtstring % (n, v[0], v[1], v[2])
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if options.format == "latex":
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print " \\hline\n\end{tabular}"
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elif options.format == "html":
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print "</table></body></html>"
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if options.plotfile:
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import ROOT
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ROOT.gROOT.SetStyle("Plain")
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ROOT.gStyle.SetOptFit(1)
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histogram = ROOT.TH1F("pulls", "Pulls", 60, -3, 3)
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for pull in pulls:
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histogram.Fill(pull)
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canvas = ROOT.TCanvas("asdf", "asdf", 800, 800)
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histogram.GetXaxis().SetTitle("pull")
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histogram.SetTitle("Post-fit nuisance pull distribution")
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histogram.SetMarkerStyle(20)
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histogram.SetMarkerSize(2)
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#histogram.Fit("gaus")
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histogram.Draw("pe")
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canvas.SaveAs(options.plotfile)
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########### PIER ###############
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import numpy
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if options.plotsf:
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n=0
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labels=[]
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v_s=[]
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v_b=[]
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rho=[]
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sys=[]
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x_position=[]
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y_position=[]
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for name in names:
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if ('SF' in name ):
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print name
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labels.append(name)
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n+=1
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v = table[name]
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v = [ re.sub('!','',i) for i in v ]
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if pmsub != None: v = [ re.sub(pmsub[0], pmsub[1], i) for i in v ]
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if sigsub != None: v = [ re.sub(sigsub[0], sigsub[1], i) for i in v ]
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# if (name,'b') in isFlagged: v[-3] = highlighters[isFlagged[(name,'b')]] % v[-3]
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# if (name,'s') in isFlagged: v[-2] = highlighters[isFlagged[(name,'s')]] % v[-2]
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v_b.append(v[0].split(','))
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v_s.append(v[1].split(','))
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rho.append(v[2].split(','))
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x_position.append(0.)
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y_position.append(n+0.5)
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sys.append(0.1)
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print n
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print labels
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print v_s
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print v_b
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print rho
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sf=[]
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sf_e=[]
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initial_uncertainty=0.2
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for i in v_b:
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try:
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sf.append(1+initial_uncertainty*eval(i[0]))
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sf_e.append(initial_uncertainty*eval(i[1]))
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except:
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print 'Problem evaluating the SF or the SF errors'
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print sf
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print sf_e
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c = ROOT.TCanvas("c","c",600,600)
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| 232 |
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#labels = ["W+udscg","W+b#bar{b}","Z+udscg","Z+b#bar{b}","t#bar{t}"]
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#d = numpy.array([0.94, 1.72, 1.10,1.08,1.01])
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d = numpy.array(sf)
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#e = numpy.array([0.02,0.16,0.02,0.04,0.02])
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e = numpy.array(sf_e)
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#for the moment just random systematics
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sys_e = numpy.array(sys)
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#TO FIX: Y position of the point.
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p = numpy.array(y_position)
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zero = numpy.array(x_position)
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| 242 |
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| 243 |
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h2 = ROOT.TH2F("h2","",1,0.7,2.2,5,0,5)
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h2.GetXaxis().SetTitle("Scale factor")
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| 245 |
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for i in range(n):
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h2.GetYaxis().SetBinLabel(i+1,labels[i])
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| 249 |
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g = ROOT.TGraphErrors(n,d,p,e,zero)
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g.SetFillColor(0)
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g.SetLineColor(2)
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g.SetLineWidth(3)
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g.SetMarkerStyle(21)
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g2 = ROOT.TGraphErrors(n,d,p,sys_e,zero)
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g2.SetFillColor(0)
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#g2.SetLineColor(2);
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g2.SetLineWidth(3);
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g2.SetMarkerStyle(21);
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h2.Draw(); ROOT.gStyle.SetOptStat(0);
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h2.GetXaxis().SetTitleSize(0.04);
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| 263 |
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h2.GetXaxis().SetLabelSize(0.04);
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h2.GetYaxis().SetLabelSize(0.06);
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| 265 |
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#h2.SetFillStyle(4000)
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c.SetFillStyle(4000)
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globalFitBand = ROOT.TBox(1.0, 0., 1.5, n);
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| 269 |
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globalFitBand.SetFillStyle(3013);
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| 270 |
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globalFitBand.SetFillColor(65);
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| 271 |
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globalFitBand.SetLineStyle(0);
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| 272 |
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#globalFitBand.Draw("same");
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|
|
globalFitLine = ROOT.TLine(1.0, 0., 1.0, 5.);
|
| 274 |
|
|
globalFitLine.SetLineWidth(2);
|
| 275 |
|
|
globalFitLine.SetLineColor(214);#214
|
| 276 |
|
|
globalFitLine.Draw("same");
|
| 277 |
|
|
|
| 278 |
|
|
#l2 = ROOT.TLegend(0.5, 0.82,0.92,0.95)
|
| 279 |
|
|
l2 = ROOT.TLegend(0.55, 0.65,0.80,0.87)
|
| 280 |
|
|
l2.SetLineWidth(2)
|
| 281 |
|
|
l2.SetBorderSize(0)
|
| 282 |
|
|
l2.SetFillColor(0)
|
| 283 |
|
|
l2.SetFillStyle(4000)
|
| 284 |
|
|
l2.SetTextFont(62)
|
| 285 |
|
|
l2.AddEntry(g,"Scale factor","p")
|
| 286 |
|
|
l2.AddEntry(g,"Statistical","l")
|
| 287 |
|
|
l2.AddEntry(g2,"Systematics","l")
|
| 288 |
|
|
l2.SetTextSize(0.035)
|
| 289 |
|
|
#l2.SetNColumns(2)
|
| 290 |
|
|
l2.Draw("same")
|
| 291 |
|
|
|
| 292 |
|
|
g.Draw("P same")
|
| 293 |
|
|
g2.Draw("[] same")
|
| 294 |
|
|
ROOT.gPad.SetLeftMargin(0.2)
|
| 295 |
|
|
ROOT.gPad.Update()
|
| 296 |
|
|
c.Print("histo.pdf")
|
| 297 |
|
|
|
