Tools for inclusive muon plots

Within the MuonAnalysis/Examples package, there's a set of tools that can be used to produce comparison plots for muon variables.

Overview

The tools consist of:

  • InclusiveMuonPlots: a CMSSW analyzer module that runs on a collection of muons (reco or pat ones), applies a configurable selection cut and then plots a set of standard variables for those muons (with a configurable choice of binning)
  • inclusiveMuonPlots_step2.py: a pyroot macro that can manipulate the rootfiles with the histograms produced by the above analyzer: it takes care of normalizing and stacking plots (e.g. for data vs mc), labelling and pretty-printing, and produces a summary html page with all the plots

The separation of the MC into different categories according to the origin of muons is done by another package in MuonAnalysis/MuonAssociators

Recipes NEW

The production of DATA/MC plots is normally performed in stages:

  1. Run on your input events, produce a PAT-tuple with only the interesting information
  2. Run on the PAT-tuple, produce histograms and merge the output of multiple jobs with hadd
  3. Stack histograms together to produce the final plots
You can combine the first two steps, but then you'll have to go back to the original sample every time you want to add a new variable or new muon selection, change the binning, ...

The following recipe works from CMSSW_3_8_0_patch2 (or newer), running samples reconstructed with at least CMSSW_3_6_X. If you really need older releases, you can find info below. Check out the following packages:

   MuonAnalysis/MuonAssociators V01-10-02
   MuonAnalysis/Examples V00-04-01

Production of the PAT-tuples

You might want to review:

  • the global tag
  • the skim criteria (e.g. the trigger paths to use)
  • the definition of good collision event
  • the trigger process name: most of the 2010 MC production has a non-standard trigger name
  • the event content, to save some disk space if you don't need trigger information

Filling histograms

There are two pairs of cfg files provided under MuonAnalysis/Examples/test/inclusiveMuonPlots

You can make simple changes to the first cfg file:

  • change the muon selection; if you switch to different categories of muons, you have also to change the label for the classification by hits to classByHitsTM (all tracker muons), classByHitsTMLSAT (tracker muons last station ang tight), classByHitsGlb (global muons) and classByHitsSta (standalone muons)
  • change the trigger

Stacking plots

For the more basic case, just do python inclusiveMuonPlots_step2.py data.root globalMuons -r mc.root -n integral -c "Punch(1),Ghost(8),Light(207),Heavy(67)" -O -R --RR --legend=legend.txt -o plots

This will:

  • take the data from data.root and the mc from mc.root
  • use the muon selection of the module globalMuons
  • normalize the histograms to the same area (read below for other options)
  • save the output in the plots folder
  • decompose MC in four categories, using the specified root color numbers
  • add a legend from legend.txt, add the overflow bin (-O-), add raito plots (-R) with automatic re-binning (--RR)

To make the full suite of plots for the ICHEP analysis, you can use the already provided script makePlots.sh

Working with older releases and samples

If you have to run on MC samples reconstructed with CMSSW_3_5_X follow this additional recipe before producing the PAT-tuples

addpkg SimMuon/MCTruth
cd SimMuon/MCTruth
cvs update -rV02-05-00-03 src/TrackerMuonHitExtractor.cc
scramv1 b -j 4
This is not needed for real data.

Stop The following two other recipes are not really supported, although they might work Stop

If you're running from a release between CMSSW_3_6_1_patch4 and CMSSW_3_7_X, you also need

   SimTracker/TrackAssociation V01-08-17
   SimMuon/MCTruth V02-06-03 (or V02-05-00-03 for 3.5.X samples)

The production of plots can't be done in CMSSW_3_5_X. However, you can still make the PAT-tuples from CMSSW_3_5_8 and then use a newer release to make the plots. In this case, check out

   SimGeneral/TrackingAnalysis V04-01-05
   SimTracker/TrackAssociation V01-08-17
   MuonAnalysis/Examples V00-03-03
   MuonAnalysis/MuonAssociators V01-09-01
   SimMuon/MCTruth V02-06-03 (or V02-05-00-03 for 3.5.X samples)

Detailed documentation

InclusiveMuonPlots module

The InclusiveMuonPlots module fills histograms from a collection of muons after applying a quality cut.

Configuration
Module configuration: the module requires three basic parameters:
  • muons : a cms.InputTag to a collection of reco::Muon or pat::Muon objects (or references to them)
  • selection : a cms.string with a quality cut to be applied at the muons before making the plots. All the datamembers and methods of the pat::Muon object can be used for the selection, with the usual syntax of the CMS.PhysicsTools cut parser; in particular, the muon ID selections can be applied as muonID('name')
  • primaryVertices : cms.InputTag to the collection of primary vertices, to compute the impact parameter.

Then, for each plot or set of plots, the module reads as input the binning to use: e.g. for the plot of pt, it will read the parameter ptBins (which should be a cms.vdouble containing all the bin boundaries)

There is a configuration file for the module under python/inclusiveMuonPlots_cfi.py. For some variables that don't have a natural choice of binning, you might want to rebin according to the statistics you have, following this snippet

from MuonAnalysis.Examples.inclusiveMuonPlots_cfi import *
process.inclusiveMuonPlots = cms.EDAnalyzer("InclusiveMuonPlots",
    makeInclusiveMuonPlots(1),  # change "1" to your rebin factor; it can be a fractionary number, like 0.5
    muons     = cms.InputTag('muons'),
    selection = cms.string("isGlobalMuon && muonID('GlobalMuonPromptTight')"), # or anything else
    primaryVertices = cms.InputTag("offlinePrimaryVertices"),
)

Code

A brief overview of the code:

  1. In the constructor, the module bins the plots. This is done using the function book, which reads the binning from the ParameterSet and books the TH1D using the TFileService putting it into a std::map<std::string, TH1*> from which it can be retrieved later.
  2. In the analyze module the code retrieves the muons, if necessary converts them on the fly into pat::Muons, applies the selection cut and then proceeds filling the plots
  3. There is some code in the endLuminosityBlock that was used to normalize the plots by reading the numer of good collision events; now that luminosity measurement is working, this is no longer used.

PyROOT script to print and stack the plots

The script inclusiveMuonPlots_step2.py reads one or two sets of histograms and produces plots a set of plots and an html page. Its behaviour is controlled by a set of command-line options, of which you can get a listing by invoking it with the --help argument.

Plotting a single dataset: to print the plots inside the directory dir of a rootfile file.root, do
inclusiveMuonPlots_step2.py file.root dir
The relevant command-line options are

  • You can define the output directory where to put the plots using option --out (or just -o)
  • You can include the *overflow3 and underflow bins using option --overflow (or just -O)
  • You can specify the text file containing the labels and titles of the plots with option --titles (or just -t); by default, it reads from inclusiveMuonPlots_titles.txt
  • You can print only a selected set of plots using option --select (or -s) giving as argument a regular expression to match against the plot names
  • To add a statistics box to the plots, use option --stat (or -S). It's likely to look awful, you have been warned.

Comparing datasets: to compare plots in file.root with those in ref.root, do
inclusiveMuonPlots_step2.py file.root dir -r ref.root
The relevant command-line options are

  • The reference file is specified through option --reference-file (or just -r)
  • If you want to compare with plots from a different directory, use option --reference-dir (or --rd).
  • To add for each histogram a plot of the ratio between the two distributions, use option --ratio (or -R)
The normalization is specified using the parameter --normalize (or -n), which can take the following values:
  • none: no normalization is performed; good e.g. to compare a sample with it's re-processed version to spot differences
  • integral: each plot is normalized according to the number of entries
  • manual,<X>: reference plots are scaled by a factor X (any valid floating point number, e.g. 37 or 0.012; it's up to you to compute it, e.g. from luminosity). Note that there is no space between the comma and the number.
  • external (obsolete): reference plots are scaled according to the normalization computed from the count of events in the processed lumi-sections and stored in the normalization histogram within the file

Using stacked histograms instead of a single reference histogram
The latest versions of the script can use as reference a stack of histograms instead of a single histogram if the composite option is specified. In order to do this, you need to run N+1 copies of the analyzer in the job that creates plots, one instance making the overall histogram and N instances each making a component of the stack. The procedure is better described with an example:

  • Let ref be the label of the analyzer that makes the overall histograms, and refAbc, refXyz and refPqr be the three instances that make the separate components of the stack (they must be the overall label followed by a name beginning with an uppercase letter)
  • Choose the ROOT color codes for each component (e.g. 7, 42, 99)
  • The syntax of the option will be --rd ref --composite="Abc(7),Xyz(42),Pqr(99)" (the ordering is bottom to top: "Abc" will be the lowest one, "Pqr" the top one)
    As for normal comparisons, you can omit the option specyfing the reference directory if it's the same as the dirrectory used for the main plot (i.e."data").
When using composite mode, the statistical uncertainty on the reference sample is shown as a gray crosshatched rectangle on top of the stacked plot.

Using the muon classification by hits in the plots
The main application of the stacked plots option described above is to make reference plots from MC using the muon classification by hits. The procedure to follow in this case is:

  • Run the module for the classification by hits described in the above link, and store the information into pat::Muons (as described in part 2B of the example in that twiki)
  • For a given muon selection myMuons, define four clones with extra selection cuts
    • myMuonsGhost with extra cut -5 <= userInt('%s') <= -1 (replace the %s with classByHitsGlb for global muons, classByHitsTM for tracker muons, classByHitsTMLSAT for TMLastStationAngTight, ...)
    • myMuonsPunch with extra cut 0 <= userInt('%s')) <= 1
    • myMuonsLight with extra cut userInt('%s') == 3
    • myMuonsHeavy with extra cut userInt('%s') == 4
    • If you have primary muons from W/Z/γ e.g. because you're running on TTbar mc, add also myMuonsPrimary with extra cut userInt('%s') == 5
  • Run also all the clones of the modules
  • Use this as argument of the composite option: Punch(1),Ghost(8),Light(207),Heavy(67) (possibly adding also Primary if you want it )

Adding plots to the suite

If you want to add some plots into the module, proceed as follows:

1) In the constructor, book the plots specifying a name; you can book multiple plots with the same binning.

// books a plot called <plotname>, with binning <plotname>Bins in the cfg file
book(*fs, pset, "<plotname>");    

// books two plots using the same binning
// specified as <template>Bins in the cfg file
book(*fs, pset, "<plot1>", "<template>");    
book(*fs, pset, "<plot2>", "<template>");    

2) In the analyze method, fill the plot. If your variable can be computed only under some circumstances (e.g. muons with a standalone track) or requires something besides the track (e.g. the TrackExtra to use the outerPosition() method), please the code by checking isNonnull() and isAvailable() so that the module is still usable on all muons.

if (mu.globalTrack().isNonnull() && mu.globalTrack()->extra().isAvailable()) {
   plots["glbOuterRho"]->Fill(mu.globalTrack()->outerPosition().Rho());
}

3) Add the parameter in the cfi file python/inclusiveMuonPlots_cfi.py, in the makeInclusiveMuonPlots function:

  • for even binning of a given size, times rebin factor, you can use plotName = _evenBins(low, high, binsize * rebinFactor)
  • for fixed binning you can use plotName = _nBins(n, low, high)
  • for some ad-hoc binning, just define plotName = cms.vdouble(... all bin edges ...)

4) Add the pretty title and x-axis label for the plot in test/inclusiveMuonPlots_titles.txt (it's not required, but it's nice to have).
The syntax is simply plotName: x-label: title; you can use ROOT's syntax for subscripts, superscripts and symbols.

5) Write to RiccardoBellan to get cvs access to the MuonAnalysis/Examples package so that you can commit the code and eveybody can benefit from your work.

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Topic revision: r9 - 2010-12-15 - RiccardoBellan
 
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