### Run jet_inspect_profiles_x with configuration files

Source: https://context7.com/cms-jet/jetmetanalysis/llms.txt

Example of running the jet_inspect_profiles_x executable using two configuration files and specifying input files and batch mode.

```bash
jet_inspect_profiles_x config/jip_vseta.config config/jip_ptrsp.config \
    -inputs JRA.root \
    -batch true
```

--------------------------------

### Configuration File Example for CommandLine Utility

Source: https://github.com/cms-jet/jetmetanalysis/blob/master/JetAnalyzers/bin/README.md

Demonstrates the format of a configuration file used by the CommandLine utility for parameter definition. Supports comments, string values, and vector values separated by blanks. Command-line arguments can override file parameters.

```plaintext
$ comment
param = value $ another comment
str = "This is a string with blanks in it" $ that wasn't so hard
vector = 10 20 30 $ no reason why this couldn't go on for lines and lines
40 50 60
```

--------------------------------

### Standalone Environment Setup and Compilation

Source: https://github.com/cms-jet/jetmetanalysis/blob/master/README.md

Sets up a standalone environment for analyzing JetMETAnalysis output without CMSSW. This involves setting environment variables for ROOT and the standalone directory, cloning the repository, compiling necessary utilities, and running an inspection tool.

```bash
cd /path/to/work
mkdir STANDALONE; cd STANDALONE
setenv STANDALONE_DIR ${PWD}
setenv ROOTSYS /path/to/your/root
setenv PATH $ROOTSYS/bin:$STANDALONE_DIR/bin:${PATH}
setenv LD_LIBRARY_PATH $ROOTSYS/lib:$STANDALONE_DIR/lib
setenv CVSROOT user@cmscvs.cern.ch:/cvs_server/repositories/CMSSW
git clone git@github.com:cms-jet/JetMETAnalysis.git
cd JetUtilities; make; cd ..
cd JetAnalyzers; make; cd ..
rehash
jet_inspect_histos_x -input /path/to/jra.root
```

--------------------------------

### Create Ntuple using JetResponseAnalyzer.cc

Source: https://github.com/cms-jet/jetmetanalysis/blob/master/README.md

Uses the JetResponseAnalyzer.cc EDAnalyzer within a CMSSW environment to produce an ntuple. The example configuration file `run_JRA_cfg.py` should be reviewed and adjusted for parameters like global tag, algorithms, and input files.

```bash
cd JetMETAnalysis/JetAnalyzers/test/
./cmsRun run_JRA_cfg.py
```

--------------------------------

### JetMETAnalysis Command Line Configuration Utility

Source: https://context7.com/cms-jet/jetmetanalysis/llms.txt

Demonstrates the syntax and usage of configuration files for the CommandLine utility, including comments, parameter assignment, and vector parameters. Shows how to use config files with executables and override parameters.

```bash
# Configuration file syntax (params.config)
# $ This is a comment
# param = value $ inline comment
# str = "String with spaces"
# vector = 10 20 30
# 40 50 60 $ continuation on next line

# Use config file as first argument
jet_response_analyzer_x params.config -input JRA.root

# Override config file parameters via command line
jet_response_analyzer_x params.config -input different.root -output custom.root

# Vector parameters use spaces (not commas)
jet_response_analyzer_x -input JRA.root -binspt 10 20 30 50 100 -algs ak4pf ak4pfchs

# Include other config files
# In config file: include otherparams.config
```

--------------------------------

### Checkout CMSSW Packages and Build

Source: https://github.com/cms-jet/jetmetanalysis/blob/master/README.md

This snippet demonstrates how to checkout necessary packages for the JetMETCorrections module and CondFormats/JetMETObjects within the CMSSW framework, followed by building the project. It requires git-cms-addpkg and scram commands.

```bash
git-cms-addpkg JetMETCorrections/Modules
git-cms-addpkg CondFormats/JetMETObjects
scram b -j 4
```

--------------------------------

### Create Animated GIFs with jet_make_animation_x

Source: https://github.com/cms-jet/jetmetanalysis/blob/master/JetUtilities/bin/README.md

Tool to compile a sequence of images into an animated GIF. Supports configuration of loop behavior, frame delays, and output directory management.

```bash
jet_make_animation_x -inputs image1.gif image2.gif image3.gif
```

--------------------------------

### Execute Jet Closure Analysis via Command Line

Source: https://github.com/cms-jet/jetmetanalysis/blob/master/JetAnalyzers/bin/README.md

This command executes the jet_draw_closure_x binary with specific flags to configure the analysis. It enables pT-based response plotting, sets the algorithm to 'ak4pfchsl1', and specifies the histogram metric as 'median'.

```bash
jet_draw_closure_x -doPt true -doEta false -doRatioPt false -doRatioEta false -doFlavorDifference false -ptcl false -algs ak4pfchsl1 -path ./ -histMet median -outputDir ./
```

--------------------------------

### Add Jet Algorithms via addAlgorithm

Source: https://context7.com/cms-jet/jetmetanalysis/llms.txt

The addAlgorithm function is a helper utility to inject jet reconstruction and analysis sequences into a CMSSW process. It supports various jet clustering algorithms, sizes, and correction levels.

```python
from JetMETAnalysis.JetAnalyzers.addAlgorithm import addAlgorithm
import JetMETAnalysis.JetAnalyzers.Defaults_cff as Defaults

# Add uncorrected anti-kT R=0.4 PF+CHS jets
addAlgorithm(process, 'ak4pfchs', Defaults, doJetReco=True, doProducer=False)

# Add L2L3-corrected anti-kT R=0.8 PUPPI jets
addAlgorithm(process, 'ak8puppil2l3', Defaults, doJetReco=True, doProducer=False)

# Override default parameters
Defaults.JetResponseParameters.doFlavor = False
Defaults.JetResponseParameters.nRefMax = 3
```

--------------------------------

### Create Mass Distributions - jet_mass_analyzer_x

Source: https://github.com/cms-jet/jetmetanalysis/blob/master/JetAnalyzers/bin/README.md

Generates mass distributions based on particle PDG IDs of mass resonance decay products. It takes a ROOT file as input and allows specifying the resonance particle, output file, weighting options, and mass range. The 'legs' parameter is crucial for defining decay products and can use predefined strings for common resonances.

--------------------------------

### Create Jet L5 Flavor JEC

Source: https://github.com/cms-jet/jetmetanalysis/blob/master/JetAnalyzers/bin/README.md

This code creates the L5Flavor JEC. It takes a ROOT input file and generates an output ROOT file containing the L5 corrections. Optional parameters allow specifying output directory, algorithms, batch mode, flavors, era, and fitting method.

```bash
jet_l5_correction_x -input jra_f.root -era Jec11_V11
```

--------------------------------

### Fit pileup parameters with jet_synchfit_x

Source: https://github.com/cms-jet/jetmetanalysis/blob/master/JetAnalyzers/bin/README.md

Executes the jet_synchfit_x tool to perform fits on jet synchronization test data. It requires specifying input/output directories, algorithm names, and the type of fit function to be applied.

```bash
jet_synchfit_x -inputDir ./ -outputDir ./ -algo1 ak4pfchs -algo2 ak4pfchs -highPU false -useNPU false -functionType standard
```

--------------------------------

### Process Histograms with jet_add_histos_x

Source: https://github.com/cms-jet/jetmetanalysis/blob/master/JetUtilities/bin/README.md

Utility to sum histograms from ROOT files with support for custom variables, ranges, and styling options. It allows for normalization, rebining, and prefix/suffix management for output histograms.

```bash
jet_add_histos_x -input jra.root
```

--------------------------------

### Create Profile Plots

Source: https://context7.com/cms-jet/jetmetanalysis/llms.txt

Generates profile plots of response versus kinematic variables directly from trees using jet_inspect_profiles_x for quick closure validation.

```bash
jet_inspect_profiles_x -inputs JRA.root \
    -algs ak4pfchs \
    -varexpr "jtpt/refpt:jteta" \
    -nbinsx 50 \
    -xmin -5.0 \
    -xmax 5.0 \
    -ymin 0.8 \
    -ymax 1.2
```

--------------------------------

### Analyze Jet Response with jet_response_analyzer_x

Source: https://context7.com/cms-jet/jetmetanalysis/llms.txt

This standalone executable processes ROOT trees to generate response histograms. It supports command-line arguments for binning, algorithm selection, and flavor separation.

```bash
jet_response_analyzer_x JetMETAnalysis/JetAnalyzers/config/jra_dr.config -input JRA.root

jet_response_analyzer_x -input JRA.root \
    -output jra.root \
    -binspt 10 20 30 50 100 200 500 1000 \
    -binseta -5.0 -3.0 -1.3 0 1.3 3.0 5.0 \
    -drmax 0.2 \
    -dorefpt true \
    -dojetpt false
```

--------------------------------

### Configure JetResponseAnalyzer in CMSSW

Source: https://context7.com/cms-jet/jetmetanalysis/llms.txt

This Python configuration script sets up the JetResponseAnalyzer EDAnalyzer within the CMSSW framework. It defines jet algorithms, loads global conditions, and processes input ROOT files to generate jet response trees.

```python
import FWCore.ParameterSet.Config as cms

process = cms.Process("JRA")

algsizetype = {'ak': [4, 8]}
jettype = ['pf', 'pfchs', 'puppi']
corrs = ['']

algorithms = []
for k, v in algsizetype.iteritems():
    for s in v:
        for j in jettype:
            for c in corrs:
                algorithms.append(str(k + str(s) + j + c))

process.load("Configuration.StandardSequences.FrontierConditions_GlobalTag_condDBv2_cff")
process.GlobalTag.globaltag = cms.string('80X_mcRun2_asymptotic_v5_2016PixDynIneff')

process.maxEvents = cms.untracked.PSet(input = cms.untracked.int32(1000))
process.source = cms.Source("PoolSource",
    fileNames = cms.untracked.vstring('root://cmsxrootd.fnal.gov//store/mc/path/to/file.root')
)

from JetMETAnalysis.JetAnalyzers.addAlgorithm import addAlgorithm
import JetMETAnalysis.JetAnalyzers.Defaults_cff as Defaults

for algorithm in algorithms:
    addAlgorithm(process, algorithm, Defaults, doJetReco=True, doProducer=False)
```

--------------------------------

### Run CMSSW Job for Jet Response Analysis

Source: https://github.com/cms-jet/jetmetanalysis/blob/master/README.md

Executes a CMSSW job using a Python configuration script to create trees for each algorithm. Ensure the global tag is set appropriately. This is typically the first step in processing jetMET data within a CMSSW environment.

```bash
cmsRun JetMETAnalysis/JetAnalyzers/test/run_JRA_cfg.py
```

--------------------------------

### Visualize Histograms and Graphs

Source: https://context7.com/cms-jet/jetmetanalysis/llms.txt

Tools for inspecting ROOT histograms and TGraphErrors objects, allowing for algorithm comparisons, kinematic binning, and residual plotting.

```bash
# Histogram inspection
jet_inspect_histos_x -inputs jra.root \
    -algs ak4pfchs \
    -variables RelRsp:RefPt

# Graph inspection
jet_inspect_graphs_x -inputs jra_g.root \
    -algs ak4pfchs \
    -variables RelRspVsRefPt
```

--------------------------------

### Inspect Jet Definition Sequence (Python)

Source: https://github.com/cms-jet/jetmetanalysis/blob/master/JetAnalyzers/README.md

Allows inspection of the generated jet sequence for a specific algorithm (e.g., 'ak5calo') within the Python configuration. This is useful for debugging and understanding the detailed configuration of jet processing components.

```python
python -i run_JRA_cfg.py
>>> process.ak5caloSequence
```

--------------------------------

### Calculate Response and Resolution

Source: https://context7.com/cms-jet/jetmetanalysis/llms.txt

Evaluates response histograms and generates resolution graphs using jet_response_and_resolution_x, with options to fit resolution curves.

```bash
jet_response_and_resolution_x -input jra_fitted.root \
    -output jra_graphs.root \
    -dorelrsp true \
    -doabsrsp true \
    -fitres true
```

--------------------------------

### Generate Histograms from Trees

Source: https://github.com/cms-jet/jetmetanalysis/blob/master/README.md

Creates histograms from ROOT trees generated by the CMSSW job. This command uses the jet_response_analyzer_x executable with a specified configuration file and input ROOT file.

```bash
jet_response_analyzer_x JetMETAnalysis/JetAnalyzers/config/jra_dr.config -input JRA.root
```

--------------------------------

### Jet Mass Analyzer Tool

Source: https://github.com/cms-jet/jetmetanalysis/blob/master/JetAnalyzers/bin/README.md

Creates mass distributions based on the particle PDG IDs of mass resonance decay products. It allows customization of input files, resonance particle, output file, weighting, and various analysis parameters like delta R, binning, and thresholds.

```APIDOC
## jet_mass_analyzer_x

### Description
This tool allows to easily create mass distributions based on the particle pdgids of the mass resonance decay products.

### Method
Not specified (assumed to be a command-line tool)

### Endpoint
N/A (command-line tool)

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters Table
| *Parameter* | *Type*    | *Default* |*Description* |
|:------------|:----------|:----------|:--------------|
| _input_     | *string*  |           | input ROOT file, containg JRA tree(s) |
| _particle_  | *string*  | "Z"       | name of the mass resonance (for x-axis labeling, might be auto-adjusted depending on 'legs' parameter below!) |
| _output_    | *string*  |"jma.root" | name of the output root file containing the created histograms |
| _useweight_ | *bool*    | false     | wether to use (individual event) weights or not |
| _xsection_  | *float*   | 0.0       | if >0.0, each (!) event will be weighted by xsecion / number of events |
| _drmax_     | *float*   | 0.3       | maximum !DeltaR between reference and jet |
| _nbinsmass_ | *int*     | 50        | number of bins for mass histograms |
| _massmin_   | *float*   | 50.0      | minimum x-value for mass histograms (might be auto-adjusted depending on 'legs' parameter below!) |
| _massmax_   | *float*   | 150.0     | maximum x-value for mass histograms (might be auto-adjusted depending on 'legs' parameter below!) |
| _jtptmin_   | *float*   | 1.0       | minimum jet pT |
| _algs_      | *vstring* | ""        | algorithms (= directory names within ROOT file) to be considered ("" means all) |
| _legs_      | *vstring* | "5:-5"    | pdgids of mass resonance decay products (see below). Several leg configurations can be provided, e.g. "1:-2,3:-4" to catch W decays. Predefined values: "zbb", "zbb_oneslb", "zbb_bothslb", "wqq", "tbqq", "tbqq_nosl", "tbqq_slb". |

### Request Example
```
# Example with default legs for Z resonance
jet_mass_analyzer_x -input my_jra.root -particle Z -output Z_mass.root

# Example with custom legs for W decay and weighting
jet_mass_analyzer_x -input my_jra.root -legs "1:-2,3:-4" -useweight -xsection 100.0 -output W_mass.root
```

### Response
Success Response (Exit Code 0 indicates success)

### Response Example
N/A (command-line tool output is typically printed to stdout/stderr or results in file creation)
```

--------------------------------

### Generate Closure Plots

Source: https://context7.com/cms-jet/jetmetanalysis/llms.txt

Uses jet_draw_closure_x to generate closure plots for jet response against pT or eta, and to compare results between different eras or datasets.

```bash
# Response vs pT closure
jet_draw_closure_x -doPt true \
    -algs ak4pfchsl1 \
    -path ./closure/ \
    -outputDir ./figures/ \
    -histMet median \
    -draw_guidelines true

# Response vs eta closure
jet_draw_closure_x -doEta true \
    -algs ak4pfchsl1 \
    -path ./closure/ \
    -outputDir ./figures/ \
    -outputFormat .pdf .png

# Ratio plots between eras
jet_draw_closure_x -doRatioPt true \
    -filepath1 ./closure_new/Closure_ak4pfchsl1.root \
    -filepath2 ./closure_old/Closure_ak4pfchsl1.root \
    -algo ak4pfchsl1 \
    -numerator "Summer20" \
    -denominator "Summer19" \
    -outputDir ./comparisons/ \
    -draw_residual true
```

--------------------------------

### Inspect Histograms

Source: https://github.com/cms-jet/jetmetanalysis/blob/master/README.md

Inspects histograms generated by the jet_response_analyzer_x tool. This command takes the input ROOT file containing the histograms as an argument.

```bash
jet_inspect_histos_x -input jra.root
```

--------------------------------

### jet_response_analyzer_x: Processing JetResponseAnalyzer Trees

Source: https://github.com/cms-jet/jetmetanalysis/blob/master/JetAnalyzers/bin/README.md

Processes ROOT trees generated by the JetResponseAnalyzer to produce histogram files. This allows for post-processing analysis with different cuts or binning. Output and input files organize histograms by jet algorithm.

```plaintext
# Example usage (conceptual):
# jet_response_analyzer_x -input input_tree.root -output output_histograms.root -algorithm ak4pf
```

--------------------------------

### Plot synchronization results with jet_synchplot_x

Source: https://github.com/cms-jet/jetmetanalysis/blob/master/JetAnalyzers/bin/README.md

Formats, plots, and saves histograms generated by jet_synchtest_x. This tool supports multiple output formats and configurable binning parameters for visualization.

```bash
jet_synchplot_x -inputDir ./ -algo1 ak4pfchs -algo2 ak4pfchs -outDir ./ -outputFormat .eps .png -fixedRange false -tdr true -npvRhoNpuBinWidth 10 -NBinsNpvRhoNpu 6
```

--------------------------------

### Calculate Jet L2 Relative Corrections

Source: https://github.com/cms-jet/jetmetanalysis/blob/master/JetAnalyzers/bin/README.md

This application calculates the relative energy correction (w.r.t. the barrel) as a function of jetpt in each etabin, producing a text file with the fit parameters. It can derive L2 and L3 corrections in one step if the l2l3 option is set to true. Dependencies include ROOT libraries.

```bash
jet_l2_correction_x -input jra.root
```

--------------------------------

### Configure SQLite Output Filename in CMSSW

Source: https://github.com/cms-jet/jetmetanalysis/blob/master/README.md

This code snippet shows how to configure the output SQLite filename within a CMSSW configuration file (JetCorrectionDBWriter_cfg.py). It sets the connection string for the SQLite database.

```python
process.CondDBCommon.connect = 'sqlite_file:Fall12_V7_MC.db'
```

--------------------------------

### Add Jet Definition to Configuration (Python)

Source: https://github.com/cms-jet/jetmetanalysis/blob/master/JetAnalyzers/README.md

Adds a specific jet definition to the CMS analysis process. The `doReco` flag determines whether to reconstruct jets or use existing ones from the input file. This function is central to defining how jets are processed.

```python
addAlgorithm(process,'JETDEF',doReco)
```

--------------------------------

### Configure JetCorrectorDBWriter EDAnalyzer in CMSSW

Source: https://github.com/cms-jet/jetmetanalysis/blob/master/README.md

This snippet configures an EDAnalyzer module named 'JetCorrectorDBWriter' in CMSSW. It specifies the era and algorithm for which corrections will be written to the SQLite file.

```python
process.dbWriterAK5Calo = cms.EDAnalyzer('JetCorrectorDBWriter',
    era = cms.untracked.string('Fall12_V7_MC'),
    algo = cms.untracked.string('AK5Calo')
)
```

--------------------------------

### Derive L5 Flavor Corrections

Source: https://context7.com/cms-jet/jetmetanalysis/llms.txt

Derives L5Flavor corrections for specific jet flavors (quark/gluon) using jet_l5_correction_x based on response differences.

```bash
jet_l5_correction_x -input jra_flavor_fitted.root \
    -era Summer20UL18_V5_MC \
    -output l5.root \
    -outputDir ./corrections/ \
    -algs ak4pfchs \
    -flavors uds c b g \
    -batch true
```

--------------------------------

### Define CMSSW Analysis Path

Source: https://github.com/cms-jet/jetmetanalysis/blob/master/README.md

This snippet defines the execution path for an analysis in CMSSW. It specifies which EDAnalyzer modules, such as dbWriterAK5Calo, should be included in the processing.

```python
process.p = cms.Path(
    process.dbWriterAK5Calo
    #process.dbWriterAK5PF *
    #process.dbWriterAK5PFchs *
    #process.dbWriterAK5JPT *
)
```

--------------------------------

### Apply Jet Energy Corrections (JEC) - jet_apply_jec_x

Source: https://github.com/cms-jet/jetmetanalysis/blob/master/JetAnalyzers/bin/README.md

Applies in-situ jet energy corrections (JEC) to uncorrected jet collections in input ROOT files. It uses text files to specify JEC parameters and can be configured with various correction levels and algorithms. The tool requires input and era parameters, with optional output path, JEC path, and algorithm specifications.

```bash
jet_apply_jec_x -input JRA.root -era Spring10 -levels 2,3 -output JRA_jec.root
```

--------------------------------

### Apply Jet Energy Corrections

Source: https://context7.com/cms-jet/jetmetanalysis/llms.txt

Applies specific levels of jet energy corrections (JEC) to existing ntuples using jet_apply_jec_x, supporting custom paths and JEC tags.

```bash
# Apply L2L3 corrections
jet_apply_jec_x -input JRA.root \
    -era Summer20UL18_V5_MC \
    -output JRA_corrected.root \
    -levels 2 3

# Apply full correction chain with custom path
jet_apply_jec_x -input JRA.root \
    -era Summer20UL18_V5_MC \
    -output JRA_l1l2l3.root \
    -jecpath ./corrections/ \
    -levels 1 2 3 \
    -algs ak4pf ak4pfchs ak8pfchs

# Using JEC tags
jet_apply_jec_x -input JRA.root \
    -era Summer20UL18_V5_MC \
    -output JRA_corrected.root \
    -levels 1 2 3 \
    -useTags true
```

--------------------------------

### Define JetCorrectorParametersCollection in CMSSW

Source: https://github.com/cms-jet/jetmetanalysis/blob/master/README.md

This snippet defines a PSet for a specific jet correction record and tag within the CMSSW framework. It's used to specify the database entries for a given era, correction level, and algorithm.

```python
cms.PSet(
    record = cms.string('AK5Calo'),
    tag = cms.string('       JetCorrectorParametersCollection_Fall12_V7_MC_AK5Calo'),
    label = cms.string('AK5Calo')
),
```

--------------------------------

### Jet Weighted Spectrum Analyzer Tool

Source: https://github.com/cms-jet/jetmetanalysis/blob/master/JetAnalyzers/bin/README.md

Checks the weights applied to different samples to ensure they correspond to the same integrated luminosity. It plots the combined pT spectrum of reference jets before and after applying weights.

```APIDOC
## jet_weighted_spectrum_x

### Description
In case you deal with several samples which need to be properly weighted in order to correspond to the same integrated luminosity, this tool provides a quick and simple check of the weights you are using: it plots the combined pT spectrum of all reference jets before and after taking the weights into account. Each sample needs to be provided as part of a list, where each element has the format *sample:weight*, with *filename=sample+".root"*.

### Method
Not specified (assumed to be a command-line tool)

### Endpoint
N/A (command-line tool)

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters Table
| *Parameter* | *Type*    | *Default* | *Description* |
|:------------|:----------|:----------|:--------------|
| _input_     | *vstring* |           | List of samples and their weights, format: "sample1:weight1 sample2:weight2 ...". Filename is assumed to be "sample.root". |
| _output_    | *string*  | "jws.root"| name of the output root file containing the created histograms |
| _algs_      | *vstring* | ""        | algorithms (= directory names within ROOT file) to be considered ("" means all) |
| _njets_     | *int*     | 1         | number of jets to consider for the pT spectrum |
| _ptmin_     | *float*   | 1.0       | minimum pT for jets to be considered |

### Request Example
```
jet_weighted_spectrum_x -input "ttbar:1.0 " "dy:0.5" -output pT_spectrum.root
```

### Response
Success Response (Exit Code 0 indicates success)

### Response Example
N/A (command-line tool output is typically printed to stdout/stderr or results in file creation)
```

--------------------------------

### Jet Energy Correction Tool

Source: https://github.com/cms-jet/jetmetanalysis/blob/master/JetAnalyzers/bin/README.md

Applies in-situ jet energy corrections (JEC) to uncorrected jet collections in input ROOT files without reprocessing the dataset. It supports specifying correction levels, JEC paths, and algorithms to consider.

```APIDOC
## jet_apply_jec_x

### Description
This tool allows you to apply a set of jet energy corrections (JEC) in situ, without reprocessing the dataset. Every uncorrected jet collection in the input tree(s) will be corrected according to the *levels* parameter, while corrected jet collections will be skipped. The location of the text files specifying the JEC parameters can be passed via the *jecpath* option.

### Method
Not specified (assumed to be a command-line tool)

### Endpoint
N/A (command-line tool)

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters Table
| *Parameter* | *Type*    | *Default* | *Description* |
|:------------|:----------|:----------|:--------------|
| _input_     | *string*  |           | name of the ROOT input file, one directory per algorithm |
| _era_       | *string*  |           | name of the JEC era (prefix of the paramter text files). |
| _output_    | *string*  | ""        | name of the output ROOT file, one directory per algorithm. [input]_jec.root if not specified. |
| _jecpath_   | *string*  | ""        | path to the parameter text files specifying the corrections. Will look in *$CMSSW_BASE* and *$CMSSW_RELEASE_BASE* if omitted. |
| _algs_      | *vstring* |           | list of algorithms to be considered. By default, all *uncorrected* algorithms found in the input file are considered, all corrected are skipped. |
| _levels_    | *vint32*  | 2,3       | list of correction levels to be applied, L2(Relative) and L3(Absolute) by default. |
| _useTags_   | *bool*    | true      | if true, the program will use properly formatted tags to set the FactorizedJetCorrector, rather than using a vector of JetCorrectorParameters |

### Request Example
```
jet_apply_jec_x -input JRA.root -era Spring10 -levels 2,3 -output JRA_jec.root
```

### Response
Success Response (Exit Code 0 indicates success)

### Response Example
N/A (command-line tool output is typically printed to stdout/stderr or results in file creation)
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.