From 8c2fb124ef5d60ccb45c4c01da4231c516c7da49 Mon Sep 17 00:00:00 2001 From: Jonathon Langford Date: Thu, 17 Feb 2022 10:21:21 +0100 Subject: [PATCH 01/11] Adding details for combine tag in README --- README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/README.md b/README.md index 0d188343..9d5b6a04 100644 --- a/README.md +++ b/README.md @@ -18,6 +18,11 @@ git clone git@github.com:jonathon-langford/HiggsAnalysis.git # Install Combine as per the documentation here: cms-analysis.github.io/HiggsAnalysis-CombinedLimit/ git clone git@github.com:cms-analysis/HiggsAnalysis-CombinedLimit.git HiggsAnalysis/CombinedLimit +# Checkout previous combine tag (fits are failing in latest, to be investigated!) +cd HiggsAnalysis/CombinedLimit +git checkout tags/v8.1.0 +cd ../.. + # Install Combine Harvester for parallelizing fits git clone https://github.com/cms-analysis/CombineHarvester.git CombineHarvester From 957bc1a36b9736af48a78e39a3ab0837a1fd7461 Mon Sep 17 00:00:00 2001 From: Jonathon Langford Date: Fri, 25 Feb 2022 10:09:49 +0100 Subject: [PATCH 02/11] Bug fix when not using prune option in datacard maker --- Datacard/tools/writeToDatacard.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/Datacard/tools/writeToDatacard.py b/Datacard/tools/writeToDatacard.py index f4a0e566..b1f00a45 100644 --- a/Datacard/tools/writeToDatacard.py +++ b/Datacard/tools/writeToDatacard.py @@ -17,7 +17,7 @@ def writePreamble(f,options): def writeProcesses(f,d,options): f.write("\n") # If opt.prune then remove all rows from dataFrame with prune=1 - d = d[d['prune']==0] + if options.prune: d = d[d['prune']==0] # d = Pandas DataFrame # Shapes # Loop over categories in dataframe @@ -80,8 +80,9 @@ def writeSystematic(f,d,s,options,stxsMergeScheme=None,scaleCorrScheme=None): # Else: for yield variation uncertainties... # Remove all rows from dataFrame with prune=1 (includes NoTag) - mask = (d['prune']==0) - d = d[mask] + if options.prune: + mask = (d['prune']==0) + d = d[mask] # If theory: loop over tiers else run over once tiers = [] @@ -194,8 +195,9 @@ def addSyst(l,v,s,p,c): def writeMCStatUncertainty(f,d,options): # Remove all rows from dataFrame with prune=1 (includes NoTag) - mask = (d['prune']==0) - d = d[mask] + if options.prune: + mask = (d['prune']==0) + d = d[mask] # Separate nuisance for each cat * years: ~Barlow-Beeston-Lite approach for scat in d.cat.unique(): From fb9e1299cd1608a854739b5eeed2c4d7bb6d016e Mon Sep 17 00:00:00 2001 From: Jonathon Langford Date: Thu, 3 Mar 2022 11:22:26 +0100 Subject: [PATCH 03/11] Fixing luminosity value for 2018 (UL) --- tools/commonObjects.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/tools/commonObjects.py b/tools/commonObjects.py index d7dd5e3e..a3d38f8f 100644 --- a/tools/commonObjects.py +++ b/tools/commonObjects.py @@ -13,8 +13,10 @@ # Centre of mass energy string sqrts__ = "13TeV" -# Luminosity map in fb^-1 -lumiMap = {'2016':36.33, '2017':41.48, '2018':59.35, 'combined':137.17, 'merged':137.17} +# Luminosity map in fb^-1: for using UL 2018 +lumiMap = {'2016':36.33, '2017':41.48, '2018':59.83, 'combined':137.65, 'merged':137.65} +# If using ReReco samples then switch to lumiMap below (missing data in 2018 EGamma data set) +#lumiMap = {'2016':36.33, '2017':41.48, '2018':59.35, 'combined':137.17, 'merged':137.17} lumiScaleFactor = 1000. # Converting from pb to fb # Constants From e5d41683eeb00b8b8f433829b32f2735bcdebc8e Mon Sep 17 00:00:00 2001 From: jlangfor Date: Mon, 6 Jun 2022 18:48:10 +0100 Subject: [PATCH 04/11] Remove instructions for changing combine tag, latest version is working --- README.md | 5 ----- 1 file changed, 5 deletions(-) diff --git a/README.md b/README.md index 9d5b6a04..0d188343 100644 --- a/README.md +++ b/README.md @@ -18,11 +18,6 @@ git clone git@github.com:jonathon-langford/HiggsAnalysis.git # Install Combine as per the documentation here: cms-analysis.github.io/HiggsAnalysis-CombinedLimit/ git clone git@github.com:cms-analysis/HiggsAnalysis-CombinedLimit.git HiggsAnalysis/CombinedLimit -# Checkout previous combine tag (fits are failing in latest, to be investigated!) -cd HiggsAnalysis/CombinedLimit -git checkout tags/v8.1.0 -cd ../.. - # Install Combine Harvester for parallelizing fits git clone https://github.com/cms-analysis/CombineHarvester.git CombineHarvester From 5ecc3e4eb8e2b721f52ef553cf520307a7037f13 Mon Sep 17 00:00:00 2001 From: Ed Scott Date: Tue, 19 Jan 2021 16:43:05 +0000 Subject: [PATCH 05/11] add draft script for performing bias studies --- Combine/Checks/README.md | 57 +++++++++++++++ Combine/Checks/RunBiasStudy.py | 125 +++++++++++++++++++++++++++++++++ Combine/Checks/biasUtils.py | 48 +++++++++++++ 3 files changed, 230 insertions(+) create mode 100644 Combine/Checks/README.md create mode 100755 Combine/Checks/RunBiasStudy.py create mode 100644 Combine/Checks/biasUtils.py diff --git a/Combine/Checks/README.md b/Combine/Checks/README.md new file mode 100644 index 00000000..37dd7c70 --- /dev/null +++ b/Combine/Checks/README.md @@ -0,0 +1,57 @@ +# Impacts + +Impacts documentation to come here. + +# Bias studies + +Here we provide a script to perform a simple bias study. + +## Inputs + +The only pre-requisite is a workspace with a single category, +and therefore a single multipdf (the object that controls the envelope method) +and a single pdf index corresponding to the choice of functional form. + +To create this, it's simple to use the existing combineCards functionality, for example: +``` +combineCards.py Datacard.txt --ic cat_name > Datacard_cat_name.txt +``` + +This creates a .txt datacard with only categories matching the reg exp `cat_name` included. +Be careful: you will probably have to manually delete some pdfindex lines at the bottom; +the script does not know that these correspond to the analysis categories, +and therefore will leave them all in (you only want the one corresponding to the category you are studying). + +Once that is done, you can run your usual `text2workspace` command to generate the `-d, --datacard` input for this script. + +## Usage + +The script is split into three different stages: + * `-t, --toys`: throw and save a total of `-n,--nToys` toys for each of the candidate functions included in the envelope + * `-f, --fits`: fit each of those toys and extract the uncertainty + * `-p, --plots`: plot the pull distribution of the resulting fits + +You can then inspect the output plots and hope to see an approximately gaussian shape with zero mean and unit width. +Normally, provided that the absolute value of the mean is less than 0.14, this is considered satisfactory. + +The three steps can be run in one go, but it's probably safer to run them one-by-one. +Here is an example: + +``` +./RunBiasStudy.py -d Datacard_mu_ggH_cat0.root -t +./RunBiasStudy.py -d Datacard_mu_ggH_cat0.root -f -c "--cminDefaultMinimizerStrategy 0 --X-rtd MINIMIZER_freezeDisassociatedParams --X-rtd MINIMIZER_multiMin_hideConstants --X-rtd MINIMIZER_multiMin_maskConstraints --X-rtd MINIMIZER_multiMin_maskChannels=2 --freezeParameters MH" +./RunBiasStudy.py -d Datacard_mu_ggH_cat0.root -p --gaussianFit +``` +The options for the second step are passed to combine; these are recommended to get the fit to converge. +The additional option on the plotting is fairly self-explanatory; it adds a gaussian fit to the output plot. + +## More options + +There are various things one can tweak for these studies. +Here is a list of the common options: + * `-n,--nToys`: the default number of toys is 1000 per function, but can be lowered or raised. + * `-e,--expectSignal`: the injected signal strength is 1 by default, but zero can also be checked, or higher values for searches. + * `-s,--seed`: the default value of -1 finds a random seed; you can fix this for reproducility if you prefer. + * `--poi`: if your parameter of interest is called something other than `r`, say so here. + * `--split`: default number of toys to be thrown or fits to be performed in one go. Set to 500 but may need to be lowered for memory reasons if you have a more complicated fit. + * `--selectFunction`: you can specify a string here to only select certain functions for these studies (e.g. `bern` to match all Bernstein polynomials, `exp1` to match just the first-order exponential). diff --git a/Combine/Checks/RunBiasStudy.py b/Combine/Checks/RunBiasStudy.py new file mode 100755 index 00000000..86ffbcd2 --- /dev/null +++ b/Combine/Checks/RunBiasStudy.py @@ -0,0 +1,125 @@ +#!/usr/bin/env python + +from biasUtils import * + +from optparse import OptionParser +parser = OptionParser() +parser.add_option("-d","--datacard",default="Datacard.root") +parser.add_option("-w","--workspace",default="w") +parser.add_option("-t","--toys",action="store_true", default=False) +parser.add_option("-n","--nToys",default=1000,type="int") +parser.add_option("-f","--fits",action="store_true", default=False) +parser.add_option("-p","--plots",action="store_true", default=False) +parser.add_option("-e","--expectSignal",default=1.,type="float") +parser.add_option("-m","--mH",default=125.,type="float") +parser.add_option("-c","--combineOptions",default="") +parser.add_option("-s","--seed",default=-1,type="int") +parser.add_option("--dryRun",action="store_true", default=False) +parser.add_option("--poi",default="r") +parser.add_option("--split",default=500,type="int") +parser.add_option("--selectFunction",default=None) +parser.add_option("--gaussianFit",action="store_true", default=False) +(opts,args) = parser.parse_args() +print +if opts.nToys>opts.split and not opts.nToys%opts.split==0: raise RuntimeError('The number of toys %g needs to be smaller than or divisible by the split number %g'%(opts.nToys, opts.split)) + +import ROOT as r +r.gROOT.SetBatch(True) +r.gStyle.SetOptStat(2211) + +ws = r.TFile(opts.datacard).Get(opts.workspace) + +pdfs = rooArgSetToList(ws.allPdfs()) +multipdfName = None +for pdf in pdfs: + if pdf.InheritsFrom("RooMultiPdf"): + if multipdfName is not None: raiseMultiError() + multipdfName = pdf.GetName() + print 'Conduct bias study for multipdf called %s'%multipdfName +multipdf = ws.pdf(multipdfName) +print + +varlist = rooArgSetToList(ws.allCats()) +indexName = None +for var in varlist: + if var.GetName().startswith('pdfindex'): + if indexName is not None: raiseMultiError() + indexName = var.GetName() + print 'Found index called %s'%indexName +print + +from collections import OrderedDict as od +indexNameMap = od() +for ipdf in range(multipdf.getNumPdfs()): + if opts.selectFunction is not None: + if not multipdf.getPdf(ipdf).GetName().count(opts.selectFunction): continue + indexNameMap[ipdf] = multipdf.getPdf(ipdf).GetName() + +if opts.toys: + if not path.isdir('BiasToysn'): system('mkdir -p BiasToys') + toyCmdBase = 'combine -m %.4f -d %s -M GenerateOnly --expectSignal %.4f -s %g --saveToys %s '%(opts.mH, opts.datacard, opts.expectSignal, opts.seed, opts.combineOptions) + for ipdf,pdfName in indexNameMap.iteritems(): + name = shortName(pdfName) + if opts.nToys > opts.split: + for isplit in range(opts.nToys//opts.split): + toyCmd = toyCmdBase + ' -t %g -n _%s_split%g --setParameters %s=%g --freezeParameters %s'%(opts.split, name, isplit, indexName, ipdf, indexName) + run(toyCmd, dry=opts.dryRun) + system('mv higgsCombine_%s* %s'%(name, toyName(name,split=isplit))) + else: + toyCmd = toyCmdBase + ' -t %g -n _%s --setParameters %s=%g --freezeParameters %s'%(opts.nToys, name, indexName, ipdf, indexName) + run(toyCmd, dry=opts.dryRun) + system('mv higgsCombine_%s* %s'%(name, toyName(name))) +print + +if opts.fits: + if not path.isdir('BiasFits'): system('mkdir -p BiasFits') + fitCmdBase = 'combine -m %.4f -d %s -M MultiDimFit -P %s --algo singles %s '%(opts.mH, opts.datacard, opts.poi, opts.combineOptions) + for ipdf,pdfName in indexNameMap.iteritems(): + name = shortName(pdfName) + if opts.nToys > opts.split: + for isplit in range(opts.nToys//opts.split): + fitCmd = fitCmdBase + ' -t %g -n _%s_split%g --toysFile=%s'%(opts.split, name, isplit, toyName(name,split=isplit)) + run(fitCmd, dry=opts.dryRun) + system('mv higgsCombine_%s* %s'%(name, fitName(name,split=isplit))) + run('hadd %s BiasFits/*%s*split*.root'%(fitName(name),name), dry=opts.dryRun) + else: + fitCmd = fitCmdBase + ' -t %g -n _%s --toysFile=%s'%(opts.nToys, name, toyName(name)) + run(fitCmd, dry=opts.dryRun) + system('mv higgsCombine_%s* %s'%(name, fitName(name))) + +if opts.plots: + if not path.isdir('BiasPlots'): system('mkdir -p BiasPlots') + for ipdf,pdfName in indexNameMap.iteritems(): + name = shortName(pdfName) + tfile = r.TFile(fitName(name)) + tree = tfile.Get('limit') + pullHist = r.TH1F('pullsForTruth_%s'%name, 'Pull distribution using the envelope to fit %s'%name, 80, -4., 4.) + pullHist.GetXaxis().SetTitle('Pull') + pullHist.GetYaxis().SetTitle('Entries') + for itoy in range(opts.nToys): + tree.GetEntry(3*itoy) + if not getattr(tree,'quantileExpected')==-1: + raiseFailError(itoy,True) + continue + bf = getattr(tree, 'r') + tree.GetEntry(3*itoy+1) + if not abs(getattr(tree,'quantileExpected')--0.32)<0.001: + raiseFailError(itoy,True) + continue + lo = getattr(tree, 'r') + tree.GetEntry(3*itoy+2) + if not abs(getattr(tree,'quantileExpected')-0.32)<0.001: + raiseFailError(itoy,True) + continue + hi = getattr(tree, 'r') + diff = bf - opts.expectSignal + unc = 0.5 * (hi-lo) + if unc > 0.: + pullHist.Fill(diff/unc) + canv = r.TCanvas() + pullHist.Draw() + if opts.gaussianFit: + r.gStyle.SetOptFit(111) + pullHist.Fit('gaus') + canv.SaveAs('%s.pdf'%plotName(name)) + canv.SaveAs('%s.png'%plotName(name)) diff --git a/Combine/Checks/biasUtils.py b/Combine/Checks/biasUtils.py new file mode 100644 index 00000000..6cf638c2 --- /dev/null +++ b/Combine/Checks/biasUtils.py @@ -0,0 +1,48 @@ +#!/usr/bin/env python + +def rooArgSetToList(argset): ## taken from Andrea Marini's great repo here: https://github.com/amarini/rfwsutils/blob/master/wsutils.py#L300-L313 + """creates a python list with the contents of argset (which should be a RooArgSet)""" + it = argset.createIterator() + + retval = [] + while True: + obj = it.Next() + + if obj == None: + break + + retval.append(obj) + + return retval + +def raiseMultiError(lax=False): + raise RuntimeError('Found more than one multipdf here - please create a workspace with just one for these bias studies. You can use "combineCards.py Datacard.txt --ic cat_name" for this)') + +def raiseFailError(itoy, lax=False): + text = 'some fits have failed, wrong quantile for toy number %g'%itoy + if not lax: raise RuntimeError('ERROR %s'%text) + else: print 'WARNING %s'%text + +def shortName(name): + return name.split('_')[-1] + +def toyName(name, split=None): + retval = 'BiasToys/biasStudy_%s_toys.root'%name + if split is not None: + split = int(split) + retval = retval.replace(name,'%s_split%g'%(name,split)) + return retval + +def fitName(name, split=None): + retval = 'BiasFits/biasStudy_%s_fits.root'%name + if split is not None: + split = int(split) + retval = retval.replace(name,'%s_split%g'%(name,split)) + return retval + +def plotName(name): + return 'BiasPlots/biasStudy_%s_pulls'%name + +def run(cmd, dry=False): + print cmd + if not dry: system(cmd) From fb92e171e660677bd74e9c920da84fbcdbbcbf77 Mon Sep 17 00:00:00 2001 From: Jonathon Langford Date: Wed, 7 Sep 2022 11:56:44 +0200 Subject: [PATCH 06/11] Pointer to bias study scripts in README --- Combine/README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/Combine/README.md b/Combine/README.md index 4a812ac1..710356a8 100644 --- a/Combine/README.md +++ b/Combine/README.md @@ -121,3 +121,7 @@ Be careful: if you are running the observed impacts, the unblinded value of the * `AsymptoticLimits`: use for limits e.g. rare/BSM analyses * `profile2D`: 2D likelihood scan, where other pois are profiled. Separate the two pois in the `fits` option options of the input json using a comma. * `scan2D`: 2D likelihood scan, where other pois are fixed. Separate the two pois in the `fits` option options of the input json using a comma. + +## Bias studies + +Scripts/instructions for bias studies are in `Checks` folder. From 05adf27a2dab31015838af3cf6d935bf8fce9750 Mon Sep 17 00:00:00 2001 From: Jonathon Langford Date: Tue, 7 Mar 2023 12:51:45 +0100 Subject: [PATCH 07/11] Adding combine tag version to README --- README.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/README.md b/README.md index 0d188343..061cd23f 100644 --- a/README.md +++ b/README.md @@ -18,6 +18,12 @@ git clone git@github.com:jonathon-langford/HiggsAnalysis.git # Install Combine as per the documentation here: cms-analysis.github.io/HiggsAnalysis-CombinedLimit/ git clone git@github.com:cms-analysis/HiggsAnalysis-CombinedLimit.git HiggsAnalysis/CombinedLimit +# Switch to combine v8.2.0 +cd $CMSSW_BASE/src/HiggsAnalysis/CombinedLimit +git fetch origin +git checkout v8.2.0 +cd $CMSSW_BASE/src + # Install Combine Harvester for parallelizing fits git clone https://github.com/cms-analysis/CombineHarvester.git CombineHarvester From 5924174a8c503a658ca5609c66d11add390628de Mon Sep 17 00:00:00 2001 From: Matthew Knight Date: Thu, 29 Jun 2023 11:34:20 +0200 Subject: [PATCH 08/11] Update installation instructions --- README.md | 16 ++++------------ 1 file changed, 4 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index 061cd23f..67b8cac7 100644 --- a/README.md +++ b/README.md @@ -9,30 +9,22 @@ Slides from the flashgg tutorial series can be found [here](https://indico.cern. export SCRAM_ARCH=slc7_amd64_gcc700 cmsrel CMSSW_10_2_13 cd CMSSW_10_2_13/src -cmsenv -git cms-init # Install the GBRLikelihood package which contains the RooDoubleCBFast implementation -git clone git@github.com:jonathon-langford/HiggsAnalysis.git +git clone https://github.com/jonathon-langford/HiggsAnalysis.git # Install Combine as per the documentation here: cms-analysis.github.io/HiggsAnalysis-CombinedLimit/ -git clone git@github.com:cms-analysis/HiggsAnalysis-CombinedLimit.git HiggsAnalysis/CombinedLimit - -# Switch to combine v8.2.0 -cd $CMSSW_BASE/src/HiggsAnalysis/CombinedLimit -git fetch origin -git checkout v8.2.0 -cd $CMSSW_BASE/src +git clone -b v8.2.0 https://github.com/cms-analysis/HiggsAnalysis-CombinedLimit.git HiggsAnalysis/CombinedLimit # Install Combine Harvester for parallelizing fits -git clone https://github.com/cms-analysis/CombineHarvester.git CombineHarvester +git clone -b 102x https://github.com/cms-analysis/CombineHarvester.git CombineHarvester # Compile external libraries cmsenv scram b -j 9 # Install Flashgg Final Fit packages -git clone -b dev_fggfinalfits_lite git@github.com:cms-analysis/flashggFinalFit.git +git clone -b dev_fggfinalfits_lite https://github.com/cms-analysis/flashggFinalFit.git cd flashggFinalFit/ ``` From aeec9abcd702886cd16f201aaac3a88325992669 Mon Sep 17 00:00:00 2001 From: Jonathon Langford <35494417+jonathon-langford@users.noreply.github.com> Date: Wed, 12 Jul 2023 15:50:29 +0100 Subject: [PATCH 09/11] Update README.md --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index 67b8cac7..9ea9101c 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,7 @@ # Final Fits (lite) + +**NOTE** If using Final Fits with the output of HiggsDNA, then please switch to [this](https://github.com/cms-analysis/flashggFinalFit/tree/dev_higgsdnafinalfit) branch + Welcome to the new Final Fits package. Here lies a a series of scripts which are used to run the final stages of the CMS Hgg analysis: signal modelling, background modelling, datacard creation, final statistical interpretation and final result plots. Slides from the flashgg tutorial series can be found [here](https://indico.cern.ch/event/963619/contributions/4112177/attachments/2151275/3627204/finalfits_tutorial_201126.pdf) From 35d86a087b7f041addcd6b125001ba7019663121 Mon Sep 17 00:00:00 2001 From: Jonathon Langford <35494417+jonathon-langford@users.noreply.github.com> Date: Thu, 1 Feb 2024 10:56:25 +0000 Subject: [PATCH 10/11] Fix for theory weights from HiggsDNA (backward compatible) --- Trees2WS/trees2ws.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/Trees2WS/trees2ws.py b/Trees2WS/trees2ws.py index 33a846b9..015a08fc 100644 --- a/Trees2WS/trees2ws.py +++ b/Trees2WS/trees2ws.py @@ -158,7 +158,8 @@ def make_argset(_ws=None,_varNames=None): if opt.productionMode in modesToSkipTheoryWeights: dfs[ts] = pandas.DataFrame(np.ones(shape=(len(t),theoryWeightContainers[ts]))) else: - dfs[ts] = t.pandas.df(ts) + #dfs[ts] = t.pandas.df(ts) + dfs[ts] = pandas.DataFrame(np.reshape(np.array(t[ts].array()),(len(t),len(tsColumns)))) dfs[ts].columns = tsColumns # Main variables to add to nominal RooDataSets From 1f5e2a614c0ceedb618f84675fd87a9b9fc7ce62 Mon Sep 17 00:00:00 2001 From: jlangfor Date: Tue, 12 Mar 2024 16:24:08 +0000 Subject: [PATCH 11/11] Adding details and scripts for bias in significance studies --- Combine/Checks/Bias_in_significance/README.md | 57 ++++++ .../RunBiasInSignificance.py | 80 ++++++++ .../RunBiasInSignificance_hig22012.py | 193 ++++++++++++++++++ .../SummaryBiasSignificance.py | 140 +++++++++++++ .../Bias_in_significance/plot_explanation.png | Bin 0 -> 398202 bytes Combine/Checks/{ => Bias_nominal}/README.md | 0 .../Checks/{ => Bias_nominal}/RunBiasStudy.py | 0 .../Checks/{ => Bias_nominal}/biasUtils.py | 0 8 files changed, 470 insertions(+) create mode 100644 Combine/Checks/Bias_in_significance/README.md create mode 100644 Combine/Checks/Bias_in_significance/RunBiasInSignificance.py create mode 100644 Combine/Checks/Bias_in_significance/RunBiasInSignificance_hig22012.py create mode 100644 Combine/Checks/Bias_in_significance/SummaryBiasSignificance.py create mode 100644 Combine/Checks/Bias_in_significance/plot_explanation.png rename Combine/Checks/{ => Bias_nominal}/README.md (100%) rename Combine/Checks/{ => Bias_nominal}/RunBiasStudy.py (100%) rename Combine/Checks/{ => Bias_nominal}/biasUtils.py (100%) diff --git a/Combine/Checks/Bias_in_significance/README.md b/Combine/Checks/Bias_in_significance/README.md new file mode 100644 index 00000000..cfdc2c2b --- /dev/null +++ b/Combine/Checks/Bias_in_significance/README.md @@ -0,0 +1,57 @@ +# Bias in significance + +Here we provide a script to perform the bias studies developed for the HIG-22-012 analysis. + +For extremely low stat categories the nominal bias studies can fail as the fitted PDF goes negative for a large fraction of the toys, unless we truncate r at 0 which then means we get a truncated pull distribution. + +For HIG-22-012 we developed a method which looks at the **bias in significance**. In short, the method uses the significance as a metric, and compares the Type-1 error rate (fraction of toys with significance greater than some threshold) for fixed (best-fit) and floating PDF indices, where the toys are generated with the best-fit index. If the curves differ significantly between these two choices then it indicates some bias in the method. The plot also compares the Type-1 error rate to the asymptotic approximation curve, indicating whether the analysis will need to use the `HybridNew` method in the limit setting. More information is provided in `AN2022_074_v7` (section 9.5), which is attached to the HIG-22-012 CADI. + +A description of what the plots show is provided in the following slide: +![](plot_explanation.png) + +## Inputs +You can run the study for the full workspace or for each category individually. +To prepare individual category workspaces you can use the existing combineCards functionality, for example: +``` +combineCards.py Datacard.txt --ic cat_name > Datacard_cat_name.txt +``` + +This creates a .txt datacard with only categories matching the reg exp `cat_name` included. +Be careful: you will probably have to manually delete some pdfindex lines at the bottom; +the script does not know that these correspond to the analysis categories, +and therefore will leave them all in (you only want the one corresponding to the category you are studying). + +Once that is done, you can run your usual `text2workspace` command to generate the `-d, --datacard` input for this script. + +For the full workspace, just use the compiled datacard with all categories as input. + +## Usage + +The script is split into four different stages. You can configure the number of toys with the `--nToys` option. If the run-time is too long you could consider creating a stat-only workspace e.g. remove all systematics (not including the PDF indices) from the txt datacard and recompile. The bias studies are probably fine to do stat-only (for stat dominated analyses). If still too long then you will want to parallelize into separate jobs and hadd the fit results at the end. An example on how to do this for the SGE batch submission system is provided in the `RunBiasInSignificance_hig22012.py` script. + +### Setup +Do an initial fit to the workspace, fixing the signal strength (r) to zero. Save the values of the best-fit pdf indices. Combine requires at least one parameter to fit which you can define with the `--initial-fit-param` option. You can set this to any parameter in the workspace (ideally a nuisance a low impact). The default is a parameter named `lumi_13TeV_uncorrelated_2016`. +``` +python RunBiasInSignificance.py --inputWSFile Datacard.root --MH 125.38 --mode setup +``` + +### Generate toys +``` +python RunBiasInSignificance.py --inputWSFile Datacard.root --MH 125.38 --nToys N --mode generate +``` +### Fit with the PDF indices fixed +Extract significance for each toy, where the PDF is fixed to the generator PDF (best-fit from setup step). +``` +python RunBiasInSignificance.py --inputWSFile Datacard.root --MH 125.38 --nToys N --mode fixed +``` + +### Fit with the PDF indices floating +Extract significance for each toy, where the PDF index if floating +``` +python RunBiasInSignificance.py --inputWSFile Datacard.root --MH 125.38 --nToys N --mode envelope +``` + +## Plotting the bias in significance results +``` +python SummaryBiasSignificance.py +``` diff --git a/Combine/Checks/Bias_in_significance/RunBiasInSignificance.py b/Combine/Checks/Bias_in_significance/RunBiasInSignificance.py new file mode 100644 index 00000000..86edd72f --- /dev/null +++ b/Combine/Checks/Bias_in_significance/RunBiasInSignificance.py @@ -0,0 +1,80 @@ +import ROOT +import os +import glob +import re +from optparse import OptionParser +import subprocess +import json + +def rooiter(x): + iter = x.iterator() + ret = iter.Next() + while ret: + yield ret + ret = iter.Next() + +def get_options(): + parser = OptionParser() + parser.add_option('--inputWSFile', dest='inputWSFile', default='Datacard.root', help="Input workspace") + parser.add_option('--MH', dest='MH', default='125.38', help="MH") + parser.add_option('--initial-fit-param', dest='initial_fit_param', default='lumi_13TeV_uncorrelated_2016', help="Initial fit parameter (combine must have an input parameter to fit to, pick any low impact nuisance)") + parser.add_option('--nToys', dest='nToys', default=2000, type='int', help="Number of toys") + parser.add_option('--mode', dest='mode', default="setup", help="[setup,generate,fixed,envelope]") + return parser.parse_args() +(opt,args) = get_options() + +if opt.mode == "setup": + + # Get list of pdfindeices + f = ROOT.TFile(opt.inputWSFile) + w = f.Get("w") + cats = w.allCats() + pdf_index = [] + for cat in rooiter(cats): + if "pdfindex" in cat.GetName(): pdf_index.append(cat.GetName()) + f.Close() + + # Initial fit fixing params to be zero + cmd = "combine -m %s -d %s -M MultiDimFit --cminDefaultMinimizerStrategy 0 --setParameters MH=%s,r=0 --freezeParameters MH,r -P %s -n _initial --saveWorkspace --saveSpecifiedIndex %s --X-rtd MINIMIZER_freezeDisassociatedParams --X-rtd MINIMIZER_multiMin_hideConstants --X-rtd MINIMIZER_multiMin_maskConstraints --X-rtd MINIMIZER_multiMin_maskChannels=2; cd .."%(opt.MH,opt.inputWSFile,opt.MH,opt.initial_fit_param,",".join(pdf_index)) + print(cmd) + os.system(cmd) + + # Save best fit pdf indices to json file + f_res = ROOT.TFile("higgsCombine_initial.MultiDimFit.mH%s.root"%opt.MH) + t = f_res.Get("limit") + t.GetEntry(0) + pdf_index_bf = {} + for index in pdf_index: pdf_index_bf[index] = getattr(t,index) + f_res.Close() + with open("pdfindex.json","w") as jf: + json.dump(pdf_index_bf, jf) + +if opt.mode == "generate": + + cmd = "combine -m %s -d higgsCombine_initial.MultiDimFit.mH%s.root -M GenerateOnly --setParameters MH=%s --freezeParameters MH --expectSignal 0 -n _toy_ --saveToys --snapshotName MultiDimFit -t %s -s -1\n\n"%(opt.MH,opt.MH,opt.MH,opt.nToys) + print(cmd) + os.system(cmd) + + cmd = "mv higgsCombine_toy_* toys.root" + os.system(cmd) + +if opt.mode == "fixed": + + # Get pdf index and the best fit values + with open("pdfindex.json", "r") as jf: + pdf_index_bf = json.load(jf) + + pdf_index_freeze = ",".join(pdf_index_bf.keys()) + pdf_index_set = "" + for k,v in pdf_index_bf.items(): pdf_index_set += "%s=%s,"%(k,v) + pdf_index_set = pdf_index_set[:-1] + + cmd = "combine -m %s -d higgsCombine_initial.MultiDimFit.mH%s.root -M Significance --snapshotName MultiDimFit --cminDefaultMinimizerStrategy 0 --setParameters MH=%s,%s --expectSignal 0 --freezeParameters MH,%s -n _fixed_ -t %s --X-rtd MINIMIZER_freezeDisassociatedParams --X-rtd MINIMIZER_multiMin_hideConstants --X-rtd MINIMIZER_multiMin_maskConstraints --X-rtd MINIMIZER_multiMin_maskChannels=2 --toysFile toys.root --X-rtd ADDNLL_RECURSIVE=1; mv higgsCombine_fixed_* fit_fixed.root"%(opt.MH,opt.MH,opt.MH,pdf_index_set,pdf_index_freeze,opt.nToys) + print(cmd) + os.system(cmd) + +if opt.mode == "envelope": + + cmd = "combine -m %s -d higgsCombine_initial.MultiDimFit.mH%s.root -M Significance --snapshotName MultiDimFit --cminDefaultMinimizerStrategy 0 --setParameters MH=%s --expectSignal 0 --freezeParameters MH -n _envelope_ -t %s --X-rtd MINIMIZER_freezeDisassociatedParams --X-rtd MINIMIZER_multiMin_hideConstants --X-rtd MINIMIZER_multiMin_maskConstraints --X-rtd MINIMIZER_multiMin_maskChannels=2 --toysFile toys.root --X-rtd ADDNLL_RECURSIVE=1; mv higgsCombine_envelope_* fit_envelope.root"%(opt.MH,opt.MH,opt.MH,opt.nToys) + print(cmd) + os.system(cmd) diff --git a/Combine/Checks/Bias_in_significance/RunBiasInSignificance_hig22012.py b/Combine/Checks/Bias_in_significance/RunBiasInSignificance_hig22012.py new file mode 100644 index 00000000..46d06971 --- /dev/null +++ b/Combine/Checks/Bias_in_significance/RunBiasInSignificance_hig22012.py @@ -0,0 +1,193 @@ +import ROOT +import os +import glob +import re +from optparse import OptionParser +import subprocess +import json + +def rooiter(x): + iter = x.iterator() + ret = iter.Next() + while ret: + yield ret + ret = iter.Next() + +def write_preamble(_file): + _file.write("#!/bin/bash\n") + _file.write("ulimit -s unlimited\n") + _file.write("set -e\n") + _file.write("cd %s/src\n"%os.environ['CMSSW_BASE']) + _file.write("export SCRAM_ARCH=%s\n"%os.environ['SCRAM_ARCH']) + _file.write("source /cvmfs/cms.cern.ch/cmsset_default.sh\n") + _file.write("eval `scramv1 runtime -sh`\n") + _file.write("cd %s\n"%os.environ['PWD']) + +def get_options(): + parser = OptionParser() + parser.add_option('--MH', dest='MH', default='125.38', help="MH") + parser.add_option('--initial-fit-param', dest='initial_fit_param', default='lumi_13TeV_2016', help="Initial fit parameters") + parser.add_option('--nToys', dest='nToys', default=2000, type='int', help="Number of toys") + parser.add_option('--nJobs', dest='nJobs', default=10, type='int', help="Number of jobs") + parser.add_option('--mode', dest='mode', default="setup", help="[setup,generate,fixed,envelope,hadd]") + return parser.parse_args() +(opt,args) = get_options() + +if opt.mode == "setup": + + os.system("cp %s/Combine/Datacard/Datacard_ggtt_resonant_mx%smy%s.txt %s"%(opt.inputDir,opt.MX,opt.MY,run_dir)) + + if not os.path.isdir("Models%s"%ext_str): + os.system("cp -rp %s/Combine/Models ./Models%s"%(opt.inputDir,ext_str)) + + # Make stat only datacard and delete pdf indices + with open("%s/Datacard_ggtt_resonant_mx%smy%s.txt"%(run_dir,opt.MX,opt.MY), "r") as f: + lines = f.readlines() + + new_lines_all = [] + for line in lines: + line = re.sub("\./Models", "%s/Models%s"%(os.environ['PWD'],ext_str), line) + new_lines_all.append(line) + with open("%s/Datacard.txt"%run_dir, "w") as f: + for line in new_lines_all: + f.write(line) + + cmd = "cd %s; text2workspace.py Datacard.txt -m %s higgsMassRange=65,150; cd .."%(run_dir,opt.MH) + print(cmd) + os.system(cmd) + + # Get list of pdfindeices + f = ROOT.TFile("%s/Datacard.root"%run_dir) + w = f.Get("w") + cats = w.allCats() + pdf_index = [] + for cat in rooiter(cats): + if "pdfindex" in cat.GetName(): pdf_index.append(cat.GetName()) + f.Close() + + # Initial fit fixing params to be zero + cmd = "cd %s; combine -m %s -d Datacard.root -M MultiDimFit --cminDefaultMinimizerStrategy 0 --setParameters MH=%s,MY=%s,MX=%s,r=0 --freezeParameters MH,MX,MY,r -P %s -n _initial --setParameterRanges r=0,0.5 --saveWorkspace --saveSpecifiedIndex %s --X-rtd MINIMIZER_freezeDisassociatedParams --X-rtd MINIMIZER_multiMin_hideConstants --X-rtd MINIMIZER_multiMin_maskConstraints --X-rtd MINIMIZER_multiMin_maskChannels=2; cd .."%(run_dir,opt.MH,opt.MH,opt.MY,opt.MX,opt.initial_fit_param,",".join(pdf_index)) + print(cmd) + os.system(cmd) + + # Save best fit pdf indices to json file + f_res = ROOT.TFile("%s/higgsCombine_initial.MultiDimFit.mH%s.root"%(run_dir,opt.MH)) + t = f_res.Get("limit") + t.GetEntry(0) + pdf_index_bf = {} + for index in pdf_index: pdf_index_bf[index] = getattr(t,index) + f_res.Close() + with open("%s/pdfindex.json"%run_dir,"w") as jf: + json.dump(pdf_index_bf, jf) + +if opt.mode == "generate": + + if not os.path.isdir("toys"): + os.system("mkdir -p toys") + + if not os.path.isdir("jobs_toys"): + os.system("mkdir -p jobs_toys") + else: + # Remove previous jobs + os.system("rm jobs_toys/sub*") + + for i_job in range( opt.nJobs ): + f_sub_name = "jobs_toys/sub_%g.sh"%(i_job) + f_sub = open(f_sub_name, "w") + write_preamble(f_sub) + f_sub.write("combine -m %s -d higgsCombine_initial.MultiDimFit.mH%s.root -M GenerateOnly --setParameters MH=%s --freezeParameters MH --expectSignal 0 -n _toy_%g_ --saveToys --snapshotName MultiDimFit -t %s -s -1\n\n"%(opt.MH,opt.MH,opt.MH,i_job,opt.nToys)) + # Move toy to toys folder + f_sub.write("mv higgsCombine_toy_%g_* toys/toy_%g.root"%(i_job,i_job)) + f_sub.close() + + os.system("chmod 775 %s"%f_sub_name) + + f_out_name = "%s/%s"%(os.environ['PWD'],re.sub("\.sh",".out",f_sub_name)) + f_err_name = "%s/%s"%(os.environ['PWD'],re.sub("\.sh",".err",f_sub_name)) + cmd = "qsub -q hep.q -l h_rt=1:0:0 -o %s -e %s %s"%(f_out_name,f_err_name,f_sub_name) + print(cmd) + os.system(cmd) + +if opt.mode == "fixed": + + if not os.path.isdir("fits_fixed"): + os.system("mkdir -p fits_fixed") + + if not os.path.isdir("jobs_fits_fixed"): + os.system("mkdir -p jobs_fits_fixed") + else: + # Remove previous jobs + os.system("rm jobs_fits_fixed/sub*") + + # Get pdf index and the best fit values + with open("pdfindex.json", "r") as jf: + pdf_index_bf = json.load(jf) + + pdf_index_save = ",".join(pdf_index_bf.keys()) + pdf_index_freeze = ",".join(pdf_index_bf.keys()) + pdf_index_set = "" + for k,v in pdf_index_bf.items(): pdf_index_set += "%s=%s,"%(k,v) + pdf_index_set = pdf_index_set[:-1] + + for i_job in range( opt.nJobs ): + f_sub_name = "jobs_fits_fixed/sub_%g.sh"%(i_job) + f_sub = open(f_sub_name, "w") + write_preamble(f_sub) + f_sub.write("combine -m %s -d higgsCombine_initial.MultiDimFit.mH%s.root -M Significance --snapshotName MultiDimFit --cminDefaultMinimizerStrategy 0 --setParameters MH=%s,%s --expectSignal 0 --freezeParameters MH,%s --trackCats %s -n _fixed_%g_ -t %s --setParameterRanges r=0,50 --X-rtd MINIMIZER_freezeDisassociatedParams --X-rtd MINIMIZER_multiMin_hideConstants --X-rtd MINIMIZER_multiMin_maskConstraints --X-rtd MINIMIZER_multiMin_maskChannels=2 --toysFile toys/toy_%g.root --X-rtd ADDNLL_RECURSIVE=1\n\n"%(opt.MH,opt.MH,opt.MH,pdf_index_set,pdf_index_freeze,pdf_index_save,i_job,opt.nToys,i_job)) + # Move toy to toys folder + f_sub.write("mv higgsCombine_fixed_%g_* fits_fixed/fit_fixed_%g.root"%(i_job,i_job)) + f_sub.close() + + os.system("chmod 775 %s"%f_sub_name) + + f_out_name = "%s/%s"%(os.environ['PWD'],re.sub("\.sh",".out",f_sub_name)) + f_err_name = "%s/%s"%(os.environ['PWD'],re.sub("\.sh",".err",f_sub_name)) + cmd = "qsub -q hep.q -l h_rt=2:0:0 -o %s -e %s %s"%(f_out_name,f_err_name,f_sub_name) + print(cmd) + os.system(cmd) + +if opt.mode == "envelope": + + if not os.path.isdir("fits_envelope"): + os.system("mkdir -p fits_envelope") + + if not os.path.isdir("jobs_fits_envelope"): + os.system("mkdir -p jobs_fits_envelope") + else: + # Remove previous jobs + os.system("rm jobs_fits_envelope/sub*") + + # Get pdf index and the best fit values + with open("pdfindex.json", "r") as jf: + pdf_index_bf = json.load(jf) + + pdf_index_save = ",".join(pdf_index_bf.keys()) + + for i_job in range( opt.nJobs ): + f_sub_name = "jobs_fits_envelope/sub_%g.sh"%(i_job) + f_sub = open(f_sub_name, "w") + write_preamble(f_sub) + f_sub.write("combine -m %s -d higgsCombine_initial.MultiDimFit.mH%s.root -M Significance --snapshotName MultiDimFit --cminDefaultMinimizerStrategy 0 --setParameters MH=%s --expectSignal 0 --freezeParameters MH --trackCats %s -n _envelope_%g_ -t %s --setParameterRanges r=0,50 --X-rtd MINIMIZER_freezeDisassociatedParams --X-rtd MINIMIZER_multiMin_hideConstants --X-rtd MINIMIZER_multiMin_maskConstraints --X-rtd MINIMIZER_multiMin_maskChannels=2 --toysFile toys/toy_%g.root --X-rtd ADDNLL_RECURSIVE=1\n\n"%(opt.MH,opt.MH,opt.MH,pdf_index_save,i_job,opt.nToys,i_job)) + # Move toy to toys folder + f_sub.write("mv higgsCombine_envelope_%g_* fits_envelope/fit_envelope_%g.root"%(i_job,i_job)) + f_sub.close() + + os.system("chmod 775 %s"%f_sub_name) + + f_out_name = "%s/%s"%(os.environ['PWD'],re.sub("\.sh",".out",f_sub_name)) + f_err_name = "%s/%s"%(os.environ['PWD'],re.sub("\.sh",".err",f_sub_name)) + cmd = "qsub -q hep.q -l h_rt=2:0:0 -o %s -e %s %s"%(f_out_name,f_err_name,f_sub_name) + print(cmd) + os.system(cmd) + + + +if opt.mode == "hadd": + + cmd = "hadd -f fit_fixed.root fits_fixed/fit_fixed_*.root" + print(cmd) + os.system(cmd) + + cmd = "hadd -f fit_envelope.root fits_envelope/fit_envelope_*.root" + print(cmd) + os.system(cmd) diff --git a/Combine/Checks/Bias_in_significance/SummaryBiasSignificance.py b/Combine/Checks/Bias_in_significance/SummaryBiasSignificance.py new file mode 100644 index 00000000..08d19723 --- /dev/null +++ b/Combine/Checks/Bias_in_significance/SummaryBiasSignificance.py @@ -0,0 +1,140 @@ +import os +import math +from optparse import OptionParser +import json +import numpy as np +import pandas as pd +import uproot +import matplotlib +import glob +matplotlib.use("Agg") +import matplotlib.pyplot as plt +plt.rcParams["figure.figsize"] = (12.5,10) + +def get_options(): + parser = OptionParser() + parser.add_option('--use-mplhep', dest='use_mplhep', default=False, action="store_true") + return parser.parse_args() +(opt,args) = get_options() + +if opt.use_mplhep: + import mplhep + mplhep.style.use("CMS") + +if not os.path.isdir("plots"): os.system("mkdir plots") + +file_dict = { + "fixed":"fit_fixed.root", + "envelope":"fit_envelope.root" +} + +Z_points = np.linspace(0,3,100) +p_gaus = [] +for Z in Z_points: p_gaus.append( (1-math.erf(Z/math.sqrt(2)))/2 ) +p_gaus = np.array( p_gaus ) + +z_thr = 4 + +# Make figure for all plots +fig = plt.figure( figsize=(12,12) ) + +f_fixed = uproot.open(file_dict['fixed']) +f_envelope = uproot.open(file_dict['envelope']) + +z_fixed = np.array( f_fixed['limit'].arrays('limit')['limit'] ) +z_envelope = np.array( f_envelope['limit'].arrays('limit')['limit'] ) + +# Drop failed fits with incorrect Z values +z_fixed = z_fixed[z_fixedZ, dtype='float') + k_envelope = np.sum(z_envelope>Z, dtype='float') + p_fixed.append( k_fixed/N_fixed ) + p_envelope.append( k_envelope/N_envelope ) + # Bayesian error estimate for efficiency + err_fixed.append( np.sqrt( (((k_fixed+1)*(k_fixed+2))/((N_fixed+2)*(N_fixed+3))) - (((k_fixed+1)*(k_fixed+1))/((N_fixed+2)*(N_fixed+2))) ) ) + err_envelope.append( np.sqrt( (((k_envelope+1)*(k_envelope+2))/((N_envelope+2)*(N_envelope+3))) - (((k_envelope+1)*(k_envelope+1))/((N_envelope+2)*(N_envelope+2))) ) ) + +p_fixed = np.array( p_fixed ) +p_envelope = np.array( p_envelope ) +err_fixed = np.array( err_fixed ) +err_envelope = np.array( err_envelope ) + +axs = [] +left, width=0.08,0.82 +bottom = 0.1 +height = 0.18 +ax = fig.add_axes([left,bottom,width,height]) +axs.append(ax) +bottom = 0.32 +ax = fig.add_axes([left,bottom,width,height]) +axs.append(ax) +height = 0.4 +bottom = 0.55 +ax = fig.add_axes([left,bottom,width,height]) +axs.append(ax) + +axs[0].plot(Z_points, p_fixed/p_fixed, ls="-", color='red', linewidth=2 ) +axs[0].fill_between(Z_points, (p_fixed-err_fixed)/p_fixed, (p_fixed+err_fixed)/p_fixed, color='salmon', alpha=0.2 ) +axs[0].plot(Z_points, p_envelope/p_fixed, ls="-", color='mediumblue', linewidth=2 ) +axs[0].fill_between(Z_points, (p_envelope-err_envelope)/p_fixed, (p_envelope+err_envelope)/p_fixed, color='cornflowerblue', alpha=0.2 ) + +axs[0].fill_between(Z_points, 0.8, 0.9, color='orange', alpha=0.2 ) +axs[0].fill_between(Z_points, 0.9, 1.1, color='limegreen', alpha=0.2, label="$<10$%" ) +axs[0].fill_between(Z_points, 1.1, 1.2, color='orange', alpha=0.2, label="$<20$%" ) +axs[0].legend(loc="upper left", fontsize=12, frameon=True) + +axs[0].set_ylim(0.5,1.5) +axs[0].set_xlim(0,2.5) + +axs[0].set_ylabel("Ratio to fixed", fontsize=16) +axs[0].set_xlabel("Z-score", fontsize=20) + + +axs[1].plot(Z_points, p_fixed/p_gaus, ls="-", color='red', linewidth=2 ) +axs[1].fill_between(Z_points, (p_fixed-err_fixed)/p_gaus, (p_fixed+err_fixed)/p_gaus, color='salmon', alpha=0.2 ) +axs[1].plot(Z_points, p_envelope/p_gaus, ls="-", color='mediumblue', linewidth=2 ) +axs[1].fill_between(Z_points, (p_envelope-err_envelope)/p_gaus, (p_envelope+err_envelope)/p_gaus, color='cornflowerblue', alpha=0.2 ) +axs[1].plot(Z_points, p_gaus/p_gaus, ls="--", color='black', linewidth=2 ) +axs[1].fill_between(Z_points, 0.8, 0.9, color='orange', alpha=0.2 ) +axs[1].fill_between(Z_points, 0.9, 1.1, color='limegreen', alpha=0.2, label="$<10$%" ) +axs[1].fill_between(Z_points, 1.1, 1.2, color='orange', alpha=0.2, label="$<20$%" ) +axs[1].legend(loc="upper left", fontsize=12, frameon=True) + + +axs[1].set_ylim(0.5,1.5) +axs[1].set_xlim(0,2.5) + +axs[1].set_ylabel("Ratio to Gaussian", fontsize=16) +axs[1].set_xticklabels(labels=[], fontsize=0) + + +axs[2].plot(Z_points, 100*p_fixed, ls="-", label="Toys (fixed)", color='red', linewidth=2 ) +axs[2].fill_between(Z_points, 100*(p_fixed-err_fixed), 100*(p_fixed+err_fixed), color='salmon', alpha=0.2 ) +axs[2].plot(Z_points, 100*p_envelope, ls="-", label="Toys (envelope)", color='mediumblue', linewidth=2 ) +axs[2].fill_between(Z_points, 100*(p_envelope-err_envelope), 100*(p_envelope+err_envelope), color='cornflowerblue', alpha=0.2 ) +axs[2].plot(Z_points, 100*p_gaus, ls="--", label="Gaussian", color='black', linewidth=2, alpha=0.8 ) + +axs[2].set_ylim(0.1,100) +axs[2].set_xlim(0,2.5) +axs[2].set_yscale("log") + +axs[2].set_ylabel("Type-1 error rate [%]", fontsize=20) +axs[2].legend(loc='best', fontsize=16, frameon=True) + +fig.savefig("plots/summary_significance.pdf", bbox_inches="tight") +fig.savefig("plots/summary_significance.png") + +axs[1].clear() +axs[2].clear() + +fig.clf() diff --git a/Combine/Checks/Bias_in_significance/plot_explanation.png b/Combine/Checks/Bias_in_significance/plot_explanation.png new file mode 100644 index 0000000000000000000000000000000000000000..c3cca69175f591807545008e68c632922a25f79c GIT binary patch literal 398202 zcmZ6T1yq#n*7t`VdXVl8=?>{GNdaj|0g>*M?h*+FBn6d_?(UM7?h+I!>He;H&N=US zzxP?sS}ulrX70GIz4!mWf17Acbwz9pG7Ja=f~~A1rwxH1H$xzZbx>6BN|91FHTVbF zO-5M<3jXtlT1A1MN!{i3-JdzVboVlMwT9R@IyqQ#xLLYdTRXbhI=LSrwM&45Sm6iB 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