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Reference Guide
StandardHistFactoryPlotsWithCategories.C File Reference
Tutorials » RooStats Tutorials

Detailed Description

View in nbviewer Open in SWAN StandardHistFactoryPlotsWithCategories

This is a standard demo that can be used with any ROOT file prepared in the standard way. You specify:

With default parameters the macro will attempt to run the standard hist2workspace example and read the ROOT file that it produces.

The macro will scan through all the categories in a simPdf find the corresponding observable. For each category, it will loop through each of the nuisance parameters and plot

You can specify how many sigma to vary by changing nSigmaToVary. You can also change the signal rate by changing muVal.

The script produces a lot plots, you can merge them by doing:

gs -q -dNOPAUSE -dBATCH -sDEVICE=pdfwrite -sOutputFile=merged.pdf `ls *pdf`
q
float * q
Definition: THbookFile.cxx:87
␛[1mRooFit v3.60 -- Developed by Wouter Verkerke and David Kirkby␛[0m
Copyright (C) 2000-2013 NIKHEF, University of California & Stanford University
All rights reserved, please read http://roofit.sourceforge.net/license.txt
1) 0x55d0be513650 RooRealVar:: alpha_syst2 = 0 L(-5 - 5) "alpha_syst2"
2) 0x55d0be51b710 RooRealVar:: alpha_syst3 = 0 L(-5 - 5) "alpha_syst3"
3) 0x55d0be4c21d0 RooRealVar:: gamma_stat_channel1_bin_0 = 1 L(0 - 1.25) "gamma_stat_channel1_bin_0"
4) 0x55d0be4dc140 RooRealVar:: gamma_stat_channel1_bin_1 = 1 L(0 - 1.5) "gamma_stat_channel1_bin_1"
check expectedData by category
Is a simultaneous PDF
on type channel1
channelCat==channelCat::channel1
channel1 channel1
[#1] INFO:Plotting -- RooTreeData::plotOn: plotting 234 events out of 234 total events
[#1] INFO:NumericIntegration -- RooRealIntegral::init(channel1_model_Int[obs_x_channel1]) using numeric integrator RooBinIntegrator to calculate Int(obs_x_channel1)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(channel1_model_Int[obs_x_channel1]) using numeric integrator RooBinIntegrator to calculate Int(obs_x_channel1)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(channel1_model_Int[obs_x_channel1]) using numeric integrator RooBinIntegrator to calculate Int(obs_x_channel1)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(channel1_model_Int[obs_x_channel1]) using numeric integrator RooBinIntegrator to calculate Int(obs_x_channel1)
channelCat==channelCat::channel1
channel1 channel1
[#1] INFO:Plotting -- RooTreeData::plotOn: plotting 234 events out of 234 total events
[#1] INFO:NumericIntegration -- RooRealIntegral::init(channel1_model_Int[obs_x_channel1]) using numeric integrator RooBinIntegrator to calculate Int(obs_x_channel1)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(channel1_model_Int[obs_x_channel1]) using numeric integrator RooBinIntegrator to calculate Int(obs_x_channel1)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(channel1_model_Int[obs_x_channel1]) using numeric integrator RooBinIntegrator to calculate Int(obs_x_channel1)
channelCat==channelCat::channel1
channel1 channel1
[#1] INFO:Plotting -- RooTreeData::plotOn: plotting 234 events out of 234 total events
[#1] INFO:NumericIntegration -- RooRealIntegral::init(channel1_model_Int[obs_x_channel1]) using numeric integrator RooBinIntegrator to calculate Int(obs_x_channel1)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(channel1_model_Int[obs_x_channel1]) using numeric integrator RooBinIntegrator to calculate Int(obs_x_channel1)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(channel1_model_Int[obs_x_channel1]) using numeric integrator RooBinIntegrator to calculate Int(obs_x_channel1)
channelCat==channelCat::channel1
channel1 channel1
[#1] INFO:Plotting -- RooTreeData::plotOn: plotting 234 events out of 234 total events
[#1] INFO:NumericIntegration -- RooRealIntegral::init(channel1_model_Int[obs_x_channel1]) using numeric integrator RooBinIntegrator to calculate Int(obs_x_channel1)
[#0] ERROR:Eval -- RooAbsReal::logEvalError(channel1_model) evaluation error,
origin : RooRealSumPdf::channel1_model[ binWidth_obs_x_channel1_0 * L_x_signal_channel1_overallSyst_x_Exp + binWidth_obs_x_channel1_1 * L_x_background1_channel1_overallSyst_x_StatUncert + binWidth_obs_x_channel1_2 * L_x_background2_channel1_overallSyst_x_StatUncert ]
message : p.d.f normalization integral is zero or negative
server values: !funcList=(L_x_signal_channel1_overallSyst_x_Exp = 0,L_x_background1_channel1_overallSyst_x_StatUncert = 0,L_x_background2_channel1_overallSyst_x_StatUncert = 0), !coefList=(binWidth_obs_x_channel1_0 = 2,binWidth_obs_x_channel1_1 = 2,binWidth_obs_x_channel1_2 = 2)
[#0] WARNING:Plotting -- At observable [x]=1 RooRealSumPdf::channel1_model[ binWidth_obs_x_channel1_0 * L_x_signal_channel1_overallSyst_x_Exp + binWidth_obs_x_channel1_1 * L_x_background1_channel1_overallSyst_x_StatUncert + binWidth_obs_x_channel1_2 * L_x_background2_channel1_overallSyst_x_StatUncert ]
p.d.f normalization integral is zero or negative @ !funcList=(L_x_signal_channel1_overallSyst_x_Exp = 0,L_x_background1_channel1_overallSyst_x_StatUncert = 0,L_x_background2_channel1_overallSyst_x_StatUncert = 0), !coefList=(binWidth_obs_x_channel1_0 = 2,binWidth_obs_x_channel1_1 = 2,binWidth_obs_x_channel1_2 = 2)
[#0] WARNING:Plotting -- At observable [x]=1 RooRealSumPdf::channel1_model[ binWidth_obs_x_channel1_0 * L_x_signal_channel1_overallSyst_x_Exp + binWidth_obs_x_channel1_1 * L_x_background1_channel1_overallSyst_x_StatUncert + binWidth_obs_x_channel1_2 * L_x_background2_channel1_overallSyst_x_StatUncert ]
p.d.f normalization integral is zero or negative @ !funcList=(L_x_signal_channel1_overallSyst_x_Exp = 0,L_x_background1_channel1_overallSyst_x_StatUncert = 0,L_x_background2_channel1_overallSyst_x_StatUncert = 0), !coefList=(binWidth_obs_x_channel1_0 = 2,binWidth_obs_x_channel1_1 = 2,binWidth_obs_x_channel1_2 = 2)
[#0] WARNING:Plotting -- At observable [x]=1.5 RooRealSumPdf::channel1_model[ binWidth_obs_x_channel1_0 * L_x_signal_channel1_overallSyst_x_Exp + binWidth_obs_x_channel1_1 * L_x_background1_channel1_overallSyst_x_StatUncert + binWidth_obs_x_channel1_2 * L_x_background2_channel1_overallSyst_x_StatUncert ]
p.d.f normalization integral is zero or negative @ !funcList=(L_x_signal_channel1_overallSyst_x_Exp = 0,L_x_background1_channel1_overallSyst_x_StatUncert = 0,L_x_background2_channel1_overallSyst_x_StatUncert = 0), !coefList=(binWidth_obs_x_channel1_0 = 2,binWidth_obs_x_channel1_1 = 2,binWidth_obs_x_channel1_2 = 2)
[#0] WARNING:Plotting -- At observable [x]=1.5 RooRealSumPdf::channel1_model[ binWidth_obs_x_channel1_0 * L_x_signal_channel1_overallSyst_x_Exp + binWidth_obs_x_channel1_1 * L_x_background1_channel1_overallSyst_x_StatUncert + binWidth_obs_x_channel1_2 * L_x_background2_channel1_overallSyst_x_StatUncert ]
p.d.f normalization integral is zero or negative @ !funcList=(L_x_signal_channel1_overallSyst_x_Exp = 0,L_x_background1_channel1_overallSyst_x_StatUncert = 0,L_x_background2_channel1_overallSyst_x_StatUncert = 0), !coefList=(binWidth_obs_x_channel1_0 = 2,binWidth_obs_x_channel1_1 = 2,binWidth_obs_x_channel1_2 = 2)
[#0] WARNING:Plotting -- At observable [x]=2 RooRealSumPdf::channel1_model[ binWidth_obs_x_channel1_0 * L_x_signal_channel1_overallSyst_x_Exp + binWidth_obs_x_channel1_1 * L_x_background1_channel1_overallSyst_x_StatUncert + binWidth_obs_x_channel1_2 * L_x_background2_channel1_overallSyst_x_StatUncert ]
p.d.f normalization integral is zero or negative @ !funcList=(L_x_signal_channel1_overallSyst_x_Exp = 0,L_x_background1_channel1_overallSyst_x_StatUncert = 0,L_x_background2_channel1_overallSyst_x_StatUncert = 0), !coefList=(binWidth_obs_x_channel1_0 = 2,binWidth_obs_x_channel1_1 = 2,binWidth_obs_x_channel1_2 = 2)
[#0] WARNING:Plotting -- At observable [x]=2 RooRealSumPdf::channel1_model[ binWidth_obs_x_channel1_0 * L_x_signal_channel1_overallSyst_x_Exp + binWidth_obs_x_channel1_1 * L_x_background1_channel1_overallSyst_x_StatUncert + binWidth_obs_x_channel1_2 * L_x_background2_channel1_overallSyst_x_StatUncert ]
p.d.f normalization integral is zero or negative @ !funcList=(L_x_signal_channel1_overallSyst_x_Exp = 0,L_x_background1_channel1_overallSyst_x_StatUncert = 0,L_x_background2_channel1_overallSyst_x_StatUncert = 0), !coefList=(binWidth_obs_x_channel1_0 = 2,binWidth_obs_x_channel1_1 = 2,binWidth_obs_x_channel1_2 = 2)
[#0] WARNING:Plotting -- At observable [x]=1 RooRealSumPdf::channel1_model[ binWidth_obs_x_channel1_0 * L_x_signal_channel1_overallSyst_x_Exp + binWidth_obs_x_channel1_1 * L_x_background1_channel1_overallSyst_x_StatUncert + binWidth_obs_x_channel1_2 * L_x_background2_channel1_overallSyst_x_StatUncert ]
p.d.f normalization integral is zero or negative @ !funcList=(L_x_signal_channel1_overallSyst_x_Exp = 0,L_x_background1_channel1_overallSyst_x_StatUncert = 0,L_x_background2_channel1_overallSyst_x_StatUncert = 0), !coefList=(binWidth_obs_x_channel1_0 = 2,binWidth_obs_x_channel1_1 = 2,binWidth_obs_x_channel1_2 = 2)
[#0] WARNING:Plotting -- At observable [x]=1.25 RooRealSumPdf::channel1_model[ binWidth_obs_x_channel1_0 * L_x_signal_channel1_overallSyst_x_Exp + binWidth_obs_x_channel1_1 * L_x_background1_channel1_overallSyst_x_StatUncert + binWidth_obs_x_channel1_2 * L_x_background2_channel1_overallSyst_x_StatUncert ]
p.d.f normalization integral is zero or negative @ !funcList=(L_x_signal_channel1_overallSyst_x_Exp = 0,L_x_background1_channel1_overallSyst_x_StatUncert = 0,L_x_background2_channel1_overallSyst_x_StatUncert = 0), !coefList=(binWidth_obs_x_channel1_0 = 2,binWidth_obs_x_channel1_1 = 2,binWidth_obs_x_channel1_2 = 2)
[#0] WARNING:Plotting -- At observable [x]=1.5 RooRealSumPdf::channel1_model[ binWidth_obs_x_channel1_0 * L_x_signal_channel1_overallSyst_x_Exp + binWidth_obs_x_channel1_1 * L_x_background1_channel1_overallSyst_x_StatUncert + binWidth_obs_x_channel1_2 * L_x_background2_channel1_overallSyst_x_StatUncert ]
p.d.f normalization integral is zero or negative @ !funcList=(L_x_signal_channel1_overallSyst_x_Exp = 0,L_x_background1_channel1_overallSyst_x_StatUncert = 0,L_x_background2_channel1_overallSyst_x_StatUncert = 0), !coefList=(binWidth_obs_x_channel1_0 = 2,binWidth_obs_x_channel1_1 = 2,binWidth_obs_x_channel1_2 = 2)
[#0] WARNING:Plotting -- At observable [x]=1.75 RooRealSumPdf::channel1_model[ binWidth_obs_x_channel1_0 * L_x_signal_channel1_overallSyst_x_Exp + binWidth_obs_x_channel1_1 * L_x_background1_channel1_overallSyst_x_StatUncert + binWidth_obs_x_channel1_2 * L_x_background2_channel1_overallSyst_x_StatUncert ]
p.d.f normalization integral is zero or negative @ !funcList=(L_x_signal_channel1_overallSyst_x_Exp = 0,L_x_background1_channel1_overallSyst_x_StatUncert = 0,L_x_background2_channel1_overallSyst_x_StatUncert = 0), !coefList=(binWidth_obs_x_channel1_0 = 2,binWidth_obs_x_channel1_1 = 2,binWidth_obs_x_channel1_2 = 2)
[#0] WARNING:Plotting -- At observable [x]=2 RooRealSumPdf::channel1_model[ binWidth_obs_x_channel1_0 * L_x_signal_channel1_overallSyst_x_Exp + binWidth_obs_x_channel1_1 * L_x_background1_channel1_overallSyst_x_StatUncert + binWidth_obs_x_channel1_2 * L_x_background2_channel1_overallSyst_x_StatUncert ]
p.d.f normalization integral is zero or negative @ !funcList=(L_x_signal_channel1_overallSyst_x_Exp = 0,L_x_background1_channel1_overallSyst_x_StatUncert = 0,L_x_background2_channel1_overallSyst_x_StatUncert = 0), !coefList=(binWidth_obs_x_channel1_0 = 2,binWidth_obs_x_channel1_1 = 2,binWidth_obs_x_channel1_2 = 2)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(channel1_model_Int[obs_x_channel1]) using numeric integrator RooBinIntegrator to calculate Int(obs_x_channel1)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(channel1_model_Int[obs_x_channel1]) using numeric integrator RooBinIntegrator to calculate Int(obs_x_channel1)
[#0] ERROR:Eval -- RooAbsReal::logEvalError(channel1_model) evaluation error,
origin : RooRealSumPdf::channel1_model[ binWidth_obs_x_channel1_0 * L_x_signal_channel1_overallSyst_x_Exp + binWidth_obs_x_channel1_1 * L_x_background1_channel1_overallSyst_x_StatUncert + binWidth_obs_x_channel1_2 * L_x_background2_channel1_overallSyst_x_StatUncert ]
message : p.d.f normalization integral is zero or negative
server values: !funcList=(L_x_signal_channel1_overallSyst_x_Exp = 0,L_x_background1_channel1_overallSyst_x_StatUncert = 0,L_x_background2_channel1_overallSyst_x_StatUncert = 0), !coefList=(binWidth_obs_x_channel1_0 = 2,binWidth_obs_x_channel1_1 = 2,binWidth_obs_x_channel1_2 = 2)
[#0] WARNING:Plotting -- At observable [x]=1 RooRealSumPdf::channel1_model[ binWidth_obs_x_channel1_0 * L_x_signal_channel1_overallSyst_x_Exp + binWidth_obs_x_channel1_1 * L_x_background1_channel1_overallSyst_x_StatUncert + binWidth_obs_x_channel1_2 * L_x_background2_channel1_overallSyst_x_StatUncert ]
p.d.f normalization integral is zero or negative @ !funcList=(L_x_signal_channel1_overallSyst_x_Exp = 0,L_x_background1_channel1_overallSyst_x_StatUncert = 0,L_x_background2_channel1_overallSyst_x_StatUncert = 0), !coefList=(binWidth_obs_x_channel1_0 = 2,binWidth_obs_x_channel1_1 = 2,binWidth_obs_x_channel1_2 = 2)
[#0] WARNING:Plotting -- At observable [x]=1 RooRealSumPdf::channel1_model[ binWidth_obs_x_channel1_0 * L_x_signal_channel1_overallSyst_x_Exp + binWidth_obs_x_channel1_1 * L_x_background1_channel1_overallSyst_x_StatUncert + binWidth_obs_x_channel1_2 * L_x_background2_channel1_overallSyst_x_StatUncert ]
p.d.f normalization integral is zero or negative @ !funcList=(L_x_signal_channel1_overallSyst_x_Exp = 0,L_x_background1_channel1_overallSyst_x_StatUncert = 0,L_x_background2_channel1_overallSyst_x_StatUncert = 0), !coefList=(binWidth_obs_x_channel1_0 = 2,binWidth_obs_x_channel1_1 = 2,binWidth_obs_x_channel1_2 = 2)
[#0] WARNING:Plotting -- At observable [x]=1.5 RooRealSumPdf::channel1_model[ binWidth_obs_x_channel1_0 * L_x_signal_channel1_overallSyst_x_Exp + binWidth_obs_x_channel1_1 * L_x_background1_channel1_overallSyst_x_StatUncert + binWidth_obs_x_channel1_2 * L_x_background2_channel1_overallSyst_x_StatUncert ]
p.d.f normalization integral is zero or negative @ !funcList=(L_x_signal_channel1_overallSyst_x_Exp = 0,L_x_background1_channel1_overallSyst_x_StatUncert = 0,L_x_background2_channel1_overallSyst_x_StatUncert = 0), !coefList=(binWidth_obs_x_channel1_0 = 2,binWidth_obs_x_channel1_1 = 2,binWidth_obs_x_channel1_2 = 2)
[#0] WARNING:Plotting -- At observable [x]=1.5 RooRealSumPdf::channel1_model[ binWidth_obs_x_channel1_0 * L_x_signal_channel1_overallSyst_x_Exp + binWidth_obs_x_channel1_1 * L_x_background1_channel1_overallSyst_x_StatUncert + binWidth_obs_x_channel1_2 * L_x_background2_channel1_overallSyst_x_StatUncert ]
p.d.f normalization integral is zero or negative @ !funcList=(L_x_signal_channel1_overallSyst_x_Exp = 0,L_x_background1_channel1_overallSyst_x_StatUncert = 0,L_x_background2_channel1_overallSyst_x_StatUncert = 0), !coefList=(binWidth_obs_x_channel1_0 = 2,binWidth_obs_x_channel1_1 = 2,binWidth_obs_x_channel1_2 = 2)
[#0] WARNING:Plotting -- At observable [x]=2 RooRealSumPdf::channel1_model[ binWidth_obs_x_channel1_0 * L_x_signal_channel1_overallSyst_x_Exp + binWidth_obs_x_channel1_1 * L_x_background1_channel1_overallSyst_x_StatUncert + binWidth_obs_x_channel1_2 * L_x_background2_channel1_overallSyst_x_StatUncert ]
p.d.f normalization integral is zero or negative @ !funcList=(L_x_signal_channel1_overallSyst_x_Exp = 0,L_x_background1_channel1_overallSyst_x_StatUncert = 0,L_x_background2_channel1_overallSyst_x_StatUncert = 0), !coefList=(binWidth_obs_x_channel1_0 = 2,binWidth_obs_x_channel1_1 = 2,binWidth_obs_x_channel1_2 = 2)
[#0] WARNING:Plotting -- At observable [x]=2 RooRealSumPdf::channel1_model[ binWidth_obs_x_channel1_0 * L_x_signal_channel1_overallSyst_x_Exp + binWidth_obs_x_channel1_1 * L_x_background1_channel1_overallSyst_x_StatUncert + binWidth_obs_x_channel1_2 * L_x_background2_channel1_overallSyst_x_StatUncert ]
p.d.f normalization integral is zero or negative @ !funcList=(L_x_signal_channel1_overallSyst_x_Exp = 0,L_x_background1_channel1_overallSyst_x_StatUncert = 0,L_x_background2_channel1_overallSyst_x_StatUncert = 0), !coefList=(binWidth_obs_x_channel1_0 = 2,binWidth_obs_x_channel1_1 = 2,binWidth_obs_x_channel1_2 = 2)
[#0] WARNING:Plotting -- At observable [x]=1 RooRealSumPdf::channel1_model[ binWidth_obs_x_channel1_0 * L_x_signal_channel1_overallSyst_x_Exp + binWidth_obs_x_channel1_1 * L_x_background1_channel1_overallSyst_x_StatUncert + binWidth_obs_x_channel1_2 * L_x_background2_channel1_overallSyst_x_StatUncert ]
p.d.f normalization integral is zero or negative @ !funcList=(L_x_signal_channel1_overallSyst_x_Exp = 0,L_x_background1_channel1_overallSyst_x_StatUncert = 0,L_x_background2_channel1_overallSyst_x_StatUncert = 0), !coefList=(binWidth_obs_x_channel1_0 = 2,binWidth_obs_x_channel1_1 = 2,binWidth_obs_x_channel1_2 = 2)
[#0] WARNING:Plotting -- At observable [x]=1.25 RooRealSumPdf::channel1_model[ binWidth_obs_x_channel1_0 * L_x_signal_channel1_overallSyst_x_Exp + binWidth_obs_x_channel1_1 * L_x_background1_channel1_overallSyst_x_StatUncert + binWidth_obs_x_channel1_2 * L_x_background2_channel1_overallSyst_x_StatUncert ]
p.d.f normalization integral is zero or negative @ !funcList=(L_x_signal_channel1_overallSyst_x_Exp = 0,L_x_background1_channel1_overallSyst_x_StatUncert = 0,L_x_background2_channel1_overallSyst_x_StatUncert = 0), !coefList=(binWidth_obs_x_channel1_0 = 2,binWidth_obs_x_channel1_1 = 2,binWidth_obs_x_channel1_2 = 2)
[#0] WARNING:Plotting -- At observable [x]=1.5 RooRealSumPdf::channel1_model[ binWidth_obs_x_channel1_0 * L_x_signal_channel1_overallSyst_x_Exp + binWidth_obs_x_channel1_1 * L_x_background1_channel1_overallSyst_x_StatUncert + binWidth_obs_x_channel1_2 * L_x_background2_channel1_overallSyst_x_StatUncert ]
p.d.f normalization integral is zero or negative @ !funcList=(L_x_signal_channel1_overallSyst_x_Exp = 0,L_x_background1_channel1_overallSyst_x_StatUncert = 0,L_x_background2_channel1_overallSyst_x_StatUncert = 0), !coefList=(binWidth_obs_x_channel1_0 = 2,binWidth_obs_x_channel1_1 = 2,binWidth_obs_x_channel1_2 = 2)
[#0] WARNING:Plotting -- At observable [x]=1.75 RooRealSumPdf::channel1_model[ binWidth_obs_x_channel1_0 * L_x_signal_channel1_overallSyst_x_Exp + binWidth_obs_x_channel1_1 * L_x_background1_channel1_overallSyst_x_StatUncert + binWidth_obs_x_channel1_2 * L_x_background2_channel1_overallSyst_x_StatUncert ]
p.d.f normalization integral is zero or negative @ !funcList=(L_x_signal_channel1_overallSyst_x_Exp = 0,L_x_background1_channel1_overallSyst_x_StatUncert = 0,L_x_background2_channel1_overallSyst_x_StatUncert = 0), !coefList=(binWidth_obs_x_channel1_0 = 2,binWidth_obs_x_channel1_1 = 2,binWidth_obs_x_channel1_2 = 2)
[#0] WARNING:Plotting -- At observable [x]=2 RooRealSumPdf::channel1_model[ binWidth_obs_x_channel1_0 * L_x_signal_channel1_overallSyst_x_Exp + binWidth_obs_x_channel1_1 * L_x_background1_channel1_overallSyst_x_StatUncert + binWidth_obs_x_channel1_2 * L_x_background2_channel1_overallSyst_x_StatUncert ]
p.d.f normalization integral is zero or negative @ !funcList=(L_x_signal_channel1_overallSyst_x_Exp = 0,L_x_background1_channel1_overallSyst_x_StatUncert = 0,L_x_background2_channel1_overallSyst_x_StatUncert = 0), !coefList=(binWidth_obs_x_channel1_0 = 2,binWidth_obs_x_channel1_1 = 2,binWidth_obs_x_channel1_2 = 2)
#include "TFile.h"
#include "TROOT.h"
#include "TCanvas.h"
#include "TList.h"
#include "TMath.h"
#include "TSystem.h"
#include "RooWorkspace.h"
#include "RooAbsData.h"
#include "RooRealVar.h"
#include "RooPlot.h"
#include "RooSimultaneous.h"
#include "RooCategory.h"
#include "RooStats/ModelConfig.h"
#include "RooStats/ProfileInspector.h"
using namespace RooFit;
using namespace RooStats;
using namespace std;
void StandardHistFactoryPlotsWithCategories(const char* infile = "",
const char* workspaceName = "combined",
const char* modelConfigName = "ModelConfig",
const char* dataName = "obsData"){
double nSigmaToVary=5.;
double muVal=0;
bool doFit=false;
// -------------------------------------------------------
// First part is just to access a user-defined file
// or create the standard example file if it doesn't exist
const char* filename = "";
if (!strcmp(infile,"")) {
filename = "results/example_combined_GaussExample_model.root";
bool fileExist = !gSystem->AccessPathName(filename); // note opposite return code
// if file does not exists generate with histfactory
if (!fileExist) {
#ifdef _WIN32
cout << "HistFactory file cannot be generated on Windows - exit" << endl;
return;
#endif
// Normally this would be run on the command line
cout <<"will run standard hist2workspace example"<<endl;
gROOT->ProcessLine(".! prepareHistFactory .");
gROOT->ProcessLine(".! hist2workspace config/example.xml");
cout <<"\n\n---------------------"<<endl;
cout <<"Done creating example input"<<endl;
cout <<"---------------------\n\n"<<endl;
}
}
else
filename = infile;
// Try to open the file
TFile *file = TFile::Open(filename);
// if input file was specified byt not found, quit
if(!file ){
cout <<"StandardRooStatsDemoMacro: Input file " << filename << " is not found" << endl;
return;
}
// -------------------------------------------------------
// Tutorial starts here
// -------------------------------------------------------
// get the workspace out of the file
RooWorkspace* w = (RooWorkspace*) file->Get(workspaceName);
if(!w){
cout <<"workspace not found" << endl;
return;
}
// get the modelConfig out of the file
ModelConfig* mc = (ModelConfig*) w->obj(modelConfigName);
// get the modelConfig out of the file
RooAbsData* data = w->data(dataName);
// make sure ingredients are found
if(!data || !mc){
w->Print();
cout << "data or ModelConfig was not found" <<endl;
return;
}
// -------------------------------------------------------
// now use the profile inspector
RooRealVar* obs = (RooRealVar*)mc->GetObservables()->first();
TList* list = new TList();
RooRealVar * firstPOI = dynamic_cast<RooRealVar*>(mc->GetParametersOfInterest()->first());
firstPOI->setVal(muVal);
// firstPOI->setConstant();
if(doFit){
mc->GetPdf()->fitTo(*data);
}
// -------------------------------------------------------
mc->GetNuisanceParameters()->Print("v");
int nPlotsMax = 1000;
cout <<" check expectedData by category"<<endl;
RooDataSet* simData=NULL;
RooSimultaneous* simPdf = NULL;
if(strcmp(mc->GetPdf()->ClassName(),"RooSimultaneous")==0){
cout <<"Is a simultaneous PDF"<<endl;
simPdf = (RooSimultaneous *)(mc->GetPdf());
} else {
cout <<"Is not a simultaneous PDF"<<endl;
}
if(doFit) {
RooCategory* channelCat = (RooCategory*) (&simPdf->indexCat());
TIterator* iter = channelCat->typeIterator() ;
RooCatType* tt = NULL;
tt=(RooCatType*) iter->Next();
RooAbsPdf* pdftmp = ((RooSimultaneous*)mc->GetPdf())->getPdf(tt->GetName()) ;
RooArgSet* obstmp = pdftmp->getObservables(*mc->GetObservables()) ;
obs = ((RooRealVar*)obstmp->first());
RooPlot* frame = obs->frame();
cout <<Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())<<endl;
cout << tt->GetName() << " " << channelCat->getLabel() <<endl;
data->plotOn(frame,MarkerSize(1),Cut(Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())),DataError(RooAbsData::None));
Double_t normCount = data->sumEntries(Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())) ;
pdftmp->plotOn(frame,LineWidth(2.),Normalization(normCount,RooAbsReal::NumEvent)) ;
frame->Draw();
cout <<"expected events = " << mc->GetPdf()->expectedEvents(*data->get()) <<endl;
return;
}
int nPlots=0;
if(!simPdf){
TIterator* it = mc->GetNuisanceParameters()->createIterator();
RooRealVar* var = NULL;
while( (var = (RooRealVar*) it->Next()) != NULL){
RooPlot* frame = obs->frame();
frame->SetYTitle(var->GetName());
data->plotOn(frame,MarkerSize(1));
var->setVal(0);
mc->GetPdf()->plotOn(frame,LineWidth(1.));
var->setVal(1);
mc->GetPdf()->plotOn(frame,LineColor(kRed),LineStyle(kDashed),LineWidth(1));
var->setVal(-1);
mc->GetPdf()->plotOn(frame,LineColor(kGreen),LineStyle(kDashed),LineWidth(1));
list->Add(frame);
var->setVal(0);
}
} else {
RooCategory* channelCat = (RooCategory*) (&simPdf->indexCat());
// TIterator* iter = simPdf->indexCat().typeIterator() ;
TIterator* iter = channelCat->typeIterator() ;
RooCatType* tt = NULL;
while(nPlots<nPlotsMax && (tt=(RooCatType*) iter->Next())) {
cout << "on type " << tt->GetName() << " " << endl;
// Get pdf associated with state from simpdf
RooAbsPdf* pdftmp = simPdf->getPdf(tt->GetName()) ;
// Generate observables defined by the pdf associated with this state
RooArgSet* obstmp = pdftmp->getObservables(*mc->GetObservables()) ;
// obstmp->Print();
obs = ((RooRealVar*)obstmp->first());
TIterator* it = mc->GetNuisanceParameters()->createIterator();
RooRealVar* var = NULL;
while(nPlots<nPlotsMax && (var = (RooRealVar*) it->Next())){
TCanvas* c2 = new TCanvas("c2");
RooPlot* frame = obs->frame();
frame->SetName(Form("frame%d",nPlots));
frame->SetYTitle(var->GetName());
cout <<Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())<<endl;
cout << tt->GetName() << " " << channelCat->getLabel() <<endl;
data->plotOn(frame,MarkerSize(1),Cut(Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())),DataError(RooAbsData::None));
Double_t normCount = data->sumEntries(Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())) ;
if(strcmp(var->GetName(),"Lumi")==0){
cout <<"working on lumi"<<endl;
var->setVal(w->var("nominalLumi")->getVal());
var->Print();
} else{
var->setVal(0);
}
// w->allVars().Print("v");
// mc->GetNuisanceParameters()->Print("v");
// pdftmp->plotOn(frame,LineWidth(2.));
// mc->GetPdf()->plotOn(frame,LineWidth(2.),Slice(*channelCat,tt->GetName()),ProjWData(*data));
//pdftmp->plotOn(frame,LineWidth(2.),Slice(*channelCat,tt->GetName()),ProjWData(*data));
normCount = pdftmp->expectedEvents(*obs);
pdftmp->plotOn(frame,LineWidth(2.),Normalization(normCount,RooAbsReal::NumEvent)) ;
if(strcmp(var->GetName(),"Lumi")==0){
cout <<"working on lumi"<<endl;
var->setVal(w->var("nominalLumi")->getVal()+0.05);
var->Print();
} else{
var->setVal(nSigmaToVary);
}
// pdftmp->plotOn(frame,LineColor(kRed),LineStyle(kDashed),LineWidth(2));
// mc->GetPdf()->plotOn(frame,LineColor(kRed),LineStyle(kDashed),LineWidth(2.),Slice(*channelCat,tt->GetName()),ProjWData(*data));
//pdftmp->plotOn(frame,LineColor(kRed),LineStyle(kDashed),LineWidth(2.),Slice(*channelCat,tt->GetName()),ProjWData(*data));
normCount = pdftmp->expectedEvents(*obs);
pdftmp->plotOn(frame,LineWidth(2.),LineColor(kRed),LineStyle(kDashed),Normalization(normCount,RooAbsReal::NumEvent)) ;
if(strcmp(var->GetName(),"Lumi")==0){
cout <<"working on lumi"<<endl;
var->setVal(w->var("nominalLumi")->getVal()-0.05);
var->Print();
} else{
var->setVal(-nSigmaToVary);
}
// pdftmp->plotOn(frame,LineColor(kGreen),LineStyle(kDashed),LineWidth(2));
// mc->GetPdf()->plotOn(frame,LineColor(kGreen),LineStyle(kDashed),LineWidth(2),Slice(*channelCat,tt->GetName()),ProjWData(*data));
//pdftmp->plotOn(frame,LineColor(kGreen),LineStyle(kDashed),LineWidth(2),Slice(*channelCat,tt->GetName()),ProjWData(*data));
normCount = pdftmp->expectedEvents(*obs);
pdftmp->plotOn(frame,LineWidth(2.),LineColor(kGreen),LineStyle(kDashed),Normalization(normCount,RooAbsReal::NumEvent)) ;
// set them back to normal
if(strcmp(var->GetName(),"Lumi")==0){
cout <<"working on lumi"<<endl;
var->setVal(w->var("nominalLumi")->getVal());
var->Print();
} else{
var->setVal(0);
}
list->Add(frame);
// quit making plots
++nPlots;
frame->Draw();
c2->SaveAs(Form("%s_%s_%s.pdf",tt->GetName(),obs->GetName(),var->GetName()));
delete c2;
}
}
}
// -------------------------------------------------------
// now make plots
TCanvas* c1 = new TCanvas("c1","ProfileInspectorDemo",800,200);
if(list->GetSize()>4){
double n = list->GetSize();
int nx = (int)sqrt(n) ;
int ny = TMath::CeilNint(n/nx);
nx = TMath::CeilNint( sqrt(n) );
c1->Divide(ny,nx);
} else
c1->Divide(list->GetSize());
for(int i=0; i<list->GetSize(); ++i){
c1->cd(i+1);
list->At(i)->Draw();
}
}
Double_t
double Double_t
Definition: RtypesCore.h:55
kRed
@ kRed
Definition: Rtypes.h:63
kGreen
@ kGreen
Definition: Rtypes.h:63
kDashed
@ kDashed
Definition: TAttLine.h:48
sqrt
double sqrt(double)
gROOT
#define gROOT
Definition: TROOT.h:410
Form
char * Form(const char *fmt,...)
gSystem
R__EXTERN TSystem * gSystem
Definition: TSystem.h:540
RooAbsArg::getObservables
RooArgSet * getObservables(const RooArgSet &set, Bool_t valueOnly=kTRUE) const
Definition: RooAbsArg.h:194
RooAbsArg::Print
virtual void Print(Option_t *options=0) const
Print TNamed name and title.
Definition: RooAbsArg.h:227
RooAbsCategory::typeIterator
TIterator * typeIterator() const
Return iterator over all defined states.
Definition: RooAbsCategory.cxx:160
RooAbsCollection::first
RooAbsArg * first() const
Definition: RooAbsCollection.h:110
RooAbsData
RooAbsData is the common abstract base class for binned and unbinned datasets.
Definition: RooAbsData.h:37
RooAbsPdf
RooAbsPdf is the abstract interface for all probability density functions The class provides hybrid a...
Definition: RooAbsPdf.h:41
RooAbsPdf::expectedEvents
virtual Double_t expectedEvents(const RooArgSet *nset) const
Return expected number of events from this p.d.f for use in extended likelihood calculations.
Definition: RooAbsPdf.cxx:2855
RooAbsPdf::plotOn
virtual RooPlot * plotOn(RooPlot *frame, const RooCmdArg &arg1=RooCmdArg::none(), const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none(), const RooCmdArg &arg7=RooCmdArg::none(), const RooCmdArg &arg8=RooCmdArg::none(), const RooCmdArg &arg9=RooCmdArg::none(), const RooCmdArg &arg10=RooCmdArg::none()) const
Helper calling plotOn(RooPlot*, RooLinkedList&) const.
Definition: RooAbsPdf.h:119
RooAbsRealLValue::frame
RooPlot * frame(const RooCmdArg &arg1, const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none(), const RooCmdArg &arg7=RooCmdArg::none(), const RooCmdArg &arg8=RooCmdArg::none()) const
Create a new RooPlot on the heap with a drawing frame initialized for this object,...
Definition: RooAbsRealLValue.cxx:209
RooAbsReal::getVal
Double_t getVal(const RooArgSet *set=0) const
Evaluate object. Returns either cached value or triggers a recalculation.
Definition: RooAbsReal.h:64
RooArgSet
RooArgSet is a container object that can hold multiple RooAbsArg objects.
Definition: RooArgSet.h:28
RooCatType
RooCatType is an auxilary class for RooAbsCategory and defines a a single category state.
Definition: RooCatType.h:22
RooCategory
RooCategory represents a fundamental (non-derived) discrete value object.
Definition: RooCategory.h:24
RooCategory::getLabel
virtual const char * getLabel() const
Return label string of current state.
Definition: RooCategory.h:39
RooDataSet
RooDataSet is a container class to hold unbinned data.
Definition: RooDataSet.h:31
RooPlot
A RooPlot is a plot frame and a container for graphics objects within that frame.
Definition: RooPlot.h:41
RooPlot::SetName
void SetName(const char *name)
Set the name of the RooPlot to 'name'.
Definition: RooPlot.cxx:1082
RooPlot::SetYTitle
void SetYTitle(const char *title)
Definition: RooPlot.cxx:1160
RooPlot::Draw
virtual void Draw(Option_t *options=0)
Draw this plot and all of the elements it contains.
Definition: RooPlot.cxx:558
RooRealVar
RooRealVar represents a fundamental (non-derived) real valued object.
Definition: RooRealVar.h:36
RooRealVar::setVal
virtual void setVal(Double_t value)
Set value of variable to 'value'.
Definition: RooRealVar.cxx:204
RooSimultaneous
RooSimultaneous facilitates simultaneous fitting of multiple PDFs to subsets of a given dataset.
Definition: RooSimultaneous.h:37
RooSimultaneous::indexCat
const RooAbsCategoryLValue & indexCat() const
Definition: RooSimultaneous.h:80
RooSimultaneous::getPdf
RooAbsPdf * getPdf(const char *catName) const
Return the p.d.f associated with the given index category name.
Definition: RooSimultaneous.cxx:343
RooWorkspace
The RooWorkspace is a persistable container for RooFit projects.
Definition: RooWorkspace.h:43
RooWorkspace::data
RooAbsData * data(const char *name) const
Retrieve dataset (binned or unbinned) with given name. A null pointer is returned if not found.
Definition: RooWorkspace.cxx:1385
RooWorkspace::Print
void Print(Option_t *opts=0) const
Print contents of the workspace.
Definition: RooWorkspace.cxx:2211
RooWorkspace::var
RooRealVar * var(const char *name) const
Retrieve real-valued variable (RooRealVar) with given name. A null pointer is returned if not found.
Definition: RooWorkspace.cxx:1312
RooWorkspace::obj
TObject * obj(const char *name) const
Return any type of object (RooAbsArg, RooAbsData or generic object) with given name)
Definition: RooWorkspace.cxx:2124
TCanvas
The Canvas class.
Definition: TCanvas.h:31
TCollection::GetSize
virtual Int_t GetSize() const
Return the capacity of the collection, i.e.
Definition: TCollection.h:182
TFile
A ROOT file is a suite of consecutive data records (TKey instances) with a well defined format.
Definition: TFile.h:48
TFile::Open
static TFile * Open(const char *name, Option_t *option="", const char *ftitle="", Int_t compress=ROOT::RCompressionSetting::EDefaults::kUseGeneralPurpose, Int_t netopt=0)
Create / open a file.
Definition: TFile.cxx:3975
TIterator
Iterator abstract base class.
Definition: TIterator.h:30
TIterator::Next
virtual TObject * Next()=0
TList
A doubly linked list.
Definition: TList.h:44
TList::Add
virtual void Add(TObject *obj)
Definition: TList.h:87
TList::At
virtual TObject * At(Int_t idx) const
Returns the object at position idx. Returns 0 if idx is out of range.
Definition: TList.cxx:354
TNamed::GetName
virtual const char * GetName() const
Returns name of object.
Definition: TNamed.h:47
TObject::Draw
virtual void Draw(Option_t *option="")
Default Draw method for all objects.
Definition: TObject.cxx:195
TSystem::AccessPathName
virtual Bool_t AccessPathName(const char *path, EAccessMode mode=kFileExists)
Returns FALSE if one can access a file using the specified access mode.
Definition: TSystem.cxx:1286
c1
return c1
Definition: legend1.C:41
n
const Int_t n
Definition: legend1.C:16
c2
return c2
Definition: legend2.C:14
RooFit::LineWidth
RooCmdArg LineWidth(Width_t width)
Definition: RooGlobalFunc.cxx:57
RooFit::MarkerSize
RooCmdArg MarkerSize(Size_t size)
Definition: RooGlobalFunc.cxx:85
RooFit::Cut
RooCmdArg Cut(const char *cutSpec)
Definition: RooGlobalFunc.cxx:79
RooFit::DataError
RooCmdArg DataError(Int_t)
Definition: RooGlobalFunc.cxx:155
RooFit::LineColor
RooCmdArg LineColor(Color_t color)
Definition: RooGlobalFunc.cxx:55
RooFit::Normalization
RooCmdArg Normalization(Double_t scaleFactor)
Definition: RooGlobalFunc.cxx:50
RooFit::LineStyle
RooCmdArg LineStyle(Style_t style)
Definition: RooGlobalFunc.cxx:56
RooStats
@(#)root/roostats:$Id$ Author: George Lewis, Kyle Cranmer
Definition: Asimov.h:20
TMath::CeilNint
Int_t CeilNint(Double_t x)
Definition: TMath.h:687
file
Definition: file.py:1
std
STL namespace.
tt
auto * tt
Definition: textangle.C:16
Author
Kyle Cranmer

Definition in file StandardHistFactoryPlotsWithCategories.C.