tools package

Submodules

tools.asciiGraph module

tools.asciiGraph.asciidraw(element, labels=True, html=False, border=False)

Draw a simple ASCII graph on the screen.

tools.caching module

class tools.caching.Cache

Bases: object

a class for storing results from interpolation

static add(key, value)

n_stored = 1000

static reset()

completely reset the cache

static size()

tools.colors module

class tools.colors.Colors

Bases: object

blue

cyan

debug

error

green

info

magenta

red

reset

warn

yellow

tools.combinations module

tools.combinations.computeCombinedLikelihood(dataset, nsig, marginalize=False, deltas_rel=0.2)

compute only lBSM :param nsig: predicted signal (list) :param deltas_rel: relative uncertainty in signal (float). Default value is 20%.

tools.combinations.computeCombinedStatistics(dataset, nsig, marginalize=False, deltas_rel=0.2)

compute lBSM, lmax, and LSM in a single run :param nsig: predicted signal (list) :param deltas_rel: relative uncertainty in signal (float). Default value is 20%.

tools.combinations.getCombinedUpperLimitFor(dataset, nsig, expected=False, deltas_rel=0.2)

Get combined upper limit. If covariances are given in globalInfo then simplified likelihood is used, else if json files are given pyhf cimbination is performed.

Parameters:

• nsig – list of signal events in each signal region/dataset. The list should obey the ordering in globalInfo.datasetOrder.
• expected – return expected, not observed value
• deltas_rel – relative uncertainty in signal (float). Default value is 20%.

Returns:

upper limit on sigma*eff

tools.coverage module

class tools.coverage.GeneralElement(el, missingX, smFinalStates, bsmFinalStates)

Bases: object

This class represents a simplified (general) element which does only holds information about its even particles and decay type. The even particles are replaced/grouped by the particles defined in smFinalStates.

sortBranches()

Sort branches. The smallest branch is the first one. If branches are equal, sort accoding to decayType.

class tools.coverage.Uncovered(topoList, sqrts=None, sigmacut=0.00E+00 [fb], groupFilters={'missing (all)': <function <lambda>>, 'missing (displaced)': <function <lambda>>, 'missing (prompt)': <function <lambda>>, 'outsideGrid (all)': <function <lambda>>}, groupFactors={'missing (all)': <function <lambda>>, 'missing (displaced)': <function <lambda>>, 'missing (prompt)': <function <lambda>>, 'outsideGrid (all)': <function <lambda>>}, groupdDescriptions={'missing (all)': 'missing topologies', 'missing (displaced)': 'missing topologies with displaced decays', 'missing (prompt)': 'missing topologies with prompt decays', 'outsideGrid (all)': 'topologies outside the grid'}, smFinalStates=None, bsmFinalSates=None)

Bases: object

Wrapper object for defining and holding a list of coverage groups (UncoveredGroup objects).

The class builds a series of UncoveredGroup objects and stores them.

Inititalize the object.

Parameters:

• topoList – TopologyList object used to select elements from.
• sqrts – Value (with units) for the center of mass energy used to compute the missing cross sections. If not specified the largest value available will be used.
• sigmacut – Minimum cross-section/weight value (after applying the reweight factor) for an element to be included. The value should in fb (unitless)
• groupFilters – Dictionary containing the groups’ labels and the method for selecting elements.
• groupFactors – Dictionary containing the groups’ labels and the method for reweighting cross sections.
• groupdDescriptions – Dictionary containing the groups’ labels and strings describing the group (used for printout)
• smFinalStates – List of (inclusive) Particle or MultiParticle objects used for grouping Z2-even particles when creating GeneralElements.
• bsmFinalSates – List of (inclusive) Particle or MultiParticle objects used for grouping Z2-odd particles when creating GeneralElements.

getGroup(label)

Returns the group with the required label. If not found, returns None.

Parameters:label – String corresponding to the specific group label Returns:UncoveredGroup object which matches the label

class tools.coverage.UncoveredGroup(label, elementFilter, reweightFactor, smFinalStates, bsmFinalStates, sqrts, sigmacut=0.0)

Bases: object

Holds information about a single coverage group: criteria for selecting and grouping elements, function for reweighting cross sections, etc.

Parameters:

• label – Group label
• elementFilter – Function which takes an element as argument and returns True (False) if the element should (not) be selected.
• reweightFactor – Function which takes an element as argument and returns the reweighting factor to be applied to the element weight.
• smFinalStates – List of Particle/MultiParticle objects used to group Z2-even particles appearing in the final state
• bsmFinalStates – List of Particle/MultiParticle objects used to group Z2-odd particles appearing in the final state
• sqrts – Value (with units) for the center of mass energy used to compute the missing cross sections. If not specified the largest value available will be used.
• sigmacut – Minimum cross-section/weight value (after applying the reweight factor) for an element to be included. The value should in fb (unitless)

addToGeneralElements(el, missingX)

Adds an element to the list of missing topologies = general elements. If the element contributes to a missing topology that is already in the list, add element and weight to topology. :parameter el: element to be added :parameter missingX: missing cross-section for the element (in fb)

getMissingX(element)

Calculate total missing cross section of an element, by recursively checking if its mothers already appear in the list. :param element: Element object

Returns:missing cross section without units (in fb)

getToposFrom(topoList)

Select the elements from topoList according to self.elementFilter and build GeneralElements from the selected elements. The GeneralElement weights corresponds to the missing cross-section with double counting from compressed elements already accounted for.

getTotalXSec(sqrts=None)

Calculate total missing topology cross section at sqrts. If no sqrts is given use self.sqrts :ivar sqrts: sqrts

tools.crashReport module

class tools.crashReport.CrashReport

Bases: object

Class that handles all crash report information.

createCrashReportFile(inputFileName, parameterFileName)

Create a new SModelS crash report file.

A SModelS crash report file contains:

• a timestamp
• SModelS version
• platform information (CPU architecture, operating system, …)
• Python version
• stack trace
• input file name
• input file content
• parameter file name
• parameter file content

Parameters:

• inputFileName – relative location of the input file
• parameterFileName – relative location of the parameter file

createUnknownErrorMessage()

Create a message for an unknown error.

tools.crashReport.createStackTrace()

Return the stack trace.

tools.crashReport.readCrashReportFile(crashReportFileName)

Read a crash report file to use its input and parameter file sections for a SModelS run.

Parameters:crashReportFileName – relative location of the crash report file

tools.databaseBrowser module

class tools.databaseBrowser.Browser(database, force_txt=False)

Bases: object

Browses the database, exits if given path does not point to a valid smodels-database. Browser can be restricted to specified run or experiment.

Parameters:

• force_txt – If True forces loading the text database.
• database – Path to the database or Database object

getAttributes(showPrivate=False)

Checks for all the fields/attributes it contains as well as the attributes of its objects if they belong to smodels.experiment.

Parameters:showPrivate – if True, also returns the protected fields (_field) Returns:list of field names (strings)

getEfficiencyFor(expid, dataset, txname, massarray)

Get an efficiency for the given experimental id, the dataset name, the txname, and the massarray. Can only be used for EfficiencyMap-type experimental results. Interpolation is done, if necessary.

Parameters:

• expid – experimental id (string)
• dataset – dataset name (string)
• txname – txname (string).
• massarray – list of masses with units, e.g. [[ 400.*GeV, 100.*GeV],[400.*GeV, 100.*GeV]]

Returns:

efficiency

getULFor(expid, txname, massarray, expected=False)

Get an upper limit for the given experimental id, the txname, and the massarray. Can only be used for UL experimental results. Interpolation is done, if necessary.

Parameters:

• expid – experimental id (string)
• txname – txname (string). ONLY required for upper limit results
• massarray – list of masses with units, e.g. [[ 400.*GeV, 100.*GeV],[400.*GeV, 100.*GeV]]
• expected – If true, return expected upper limit, otherwise return observed upper limit.

Returns:

upper limit [fb]

getULForSR(expid, datasetID)

Get an upper limit for the given experimental id and dataset (signal region). Can only be used for efficiency-map results. :param expid: experimental id (string) :param datasetID: string defining the dataset id, e.g. ANA5-CUT3. :return: upper limit [fb]

getValuesFor(attribute, expResult=None)

Returns a list for the possible values appearing in the database for the required attribute (sqrts,id,constraint,…).

Parameters:

• attribute – name of a field in the database (string).
• expResult – if defined, restricts the list to the corresponding expResult. Must be an ExpResult object.

Returns:

list of values

loadAllResults()

Saves all the results from database to the _selectedExpResults. Can be used to restore all results to _selectedExpResults.

selectExpResultsWith(**restrDict)

Loads the list of the experimental results (pair of InfoFile and DataFile) satisfying the restrictions to the _selectedExpResults. The restrictions specified as a dictionary.

Parameters:restrDict – selection fields and their allowed values. E.g. lumi = [19.4/fb, 20.3/fb], txName = ‘T1’,….} The values can be single entries or a list of values. For the fields not listed, all values are assumed to be allowed.

tools.databaseBrowser.main(args)

IPython interface for browsing the Database.

tools.databaseClient module

class tools.databaseClient.DatabaseClient(servername=None, port=None, verbose='info', rundir='./', logfile='@@rundir@@/dbclient.log', clientid=-1)

Bases: object

clearCache()

findServerStatus()

getWaitingTime()

compute a waiting time between attempts, from self.ntries

initialize()

log(*args)

nameAndPort()

pprint(*args)

query(msg)

query a certain result, msg is eg. obs:ATLAS-SUSY-2016-07:ul:T1:[[5.5000E+02,4.5000E+02],[5.5000E+02,4.5000E+02]]

saveStats()

send(message, amount_expected=32)

send the message. :param amount_expected: how many return bytes do you expect

send_shutdown()

send shutdown request to server

setDefaults()

put in some defaults if data members dont exist

tools.databaseClient.stresstest(args)

this is one process in the stress test

tools.databaseServer module

class tools.databaseServer.DatabaseServer(dbpath, servername=None, port=None, verbose='info', rundir='./', logfile='@@rundir@@/dbserver.log')

Bases: object

finish()

initialize()

is_port_in_use(port)

check if port is in use

listen()

log(*args)

logServerStats()

log our stats upon exit

lookUpResult(data)

parseData(data)

parse the data packet

pprint(*args)

run(nonblocking=False)

run server :param nonblock: run in nonblocking mode (not yet implemented)

setStatus(status)

servers have a status file that tells us if they are running

shutdown(fromwhere='unknown')

tools.databaseServer.shutdownAll()

tools.externalPythonTools module

class tools.externalPythonTools.ExternalPythonTool(importname, optional=False)

Bases: object

An instance of this class represents the installation of a python package. As it is python-only, we need this only for installation, not for running (contrary to nllfast or pythia).

Initializes the ExternalPythonTool object. Useful for installation. :params optional: optional package, not needed for core SModelS.

checkInstallation()

The check is basically done in the constructor

compile()

installDirectory()

Just returns the pythonPath variable

pathOfExecutable()

Just returns the pythonPath variable

tools.inclusiveObjects module

class tools.inclusiveObjects.InclusiveList

Bases: list

An inclusive list class. It will return True when compared to any other list object.

class tools.inclusiveObjects.InclusiveValue

Bases: int

An inclusive number class. It will return True when compared to any other integer, float or Unum object.

tools.interactivePlots module

class tools.interactivePlots.Plotter(smodelsFolder, slhaFolder, parameterFile, modelFile=None)

Bases: object

A class to store the required data and produce the interactive plots

Initializes the class.

Parameters:

• smodelsFolder – path to the folder or tarball containing the smodels (python) output files
• slhaFolder – path to the folder or tarball containing the SLHA input files
• parameterFile – path to the file containing the plotting definitions
• modelFile – path to the model file, e.g smodels/share/models/mssm.py

display()

display the pages, works in jupyter notebooks only

editSlhaInformation()

Edits slha_hover_information,ctau_hover_information,BR_hover_information,variable_x,variable_y if they are defined as a list. The function transforms it in a dict whose keys are the object names

fillWith(smodelsOutput, slhaData)

Fill the dictionary (data_dict) with the desired data from the smodels output dictionary (smodelsDict) and the pyslha.Doc object slhaData

getParticleName(pdg)

looks for the particle label in the model.py file

initializeDataDict()

Initializes an empty dictionary with the plotting options.

loadData(npoints=-1)

Reads the data from the smodels and SLHA folders. If npoints > 0, it will limit the number of points in the plot to npoints.

Parameters:npoints – Number of points to be plotted (int). If < 0, all points will be used.

loadModelFile()

Reads the parameters from the plotting parameter file.

loadParameters()

Reads the parameters from the plotting parameter file.

plot(outFolder, indexfile='plots.html')

Uses the data in self.data_dict to produce the plots.

Parameters:

• outFolder – Path to the output folder.
• indexfile – name of entry webpage

rmFiles(flist)

remove files in flist

tools.interactivePlots.main(args, indexfile='index.html')

Create the interactive plots using the input from argparse

Parameters:args – argparser.Namespace object containing the options for the plotter

Main interface for the interactive-plots.

Parameters:

• smodelsFolder – Path to the folder or tarball containing the SModelS python output
• slhaFolder – Path to the folder or tarball containing the SLHA files corresponding to the SModelS output
• parameters – Path to the parameter file setting the options for the interactive plots
• npoints – Number of points used to produce the plot. If -1, all points will be used.
• verbosity – Verbosity of the output (debug,info,warning,error)
• indexfile – name of the starting web page (index.html)

Returns:

True if the plot creation was successfull

tools.interactivePlotsHelpers module

class tools.interactivePlotsHelpers.Filler(plotter, smodelsOutput, slhaData)

Bases: object

A class with the functions required to fill the data dictionary to produce the plots

getBR()

Gets the requested branching ratios from the slha file, that will go into de hover.

getCtau()

Computes the requested ctaus, that will go into de hover.

getExpres()

Extracts the Expres info from the .py output. If requested, the data will be appended on each corresponding list

getMaxMissingTopology()

Extracts the missing topology with the largest cross section

getMaxMissingTopologyXsection()

Extracts the cross section of the missing topology with the largest cross section

getOutsideGrid()

Extracts the outside grid info from the .py output. If requested, the data will be appended on each corresponding list.

getParticleName(pdg)

looks for the particle label in the model.py file

getSlhaData(variable_x, variable_y)

fills data dict with slha data

getSlhaHoverInfo()

Gets the requested slha info from each slha file, to fill the hover.

getSmodelSData()

fills data dict with smodels data

getTotalMissingDisplaced()

Extracts the Total cross section from missing displaced topologies

getTotalMissingPrompt()

Extracts the Total cross section from missing prompt topologies

getTotalMissingXsec()

Extracts the total crossection from missing topologies.

getVariable(variable)

Gets the variable from the slha file.

openSMParticles()

Loads SMparticles.py to parse over SM pdg-labels

truncate(number)

truncate float to 3 decimal places

class tools.interactivePlotsHelpers.PlotlyBackend(master, path_to_plots)

Bases: object

DataFrameExcludedNonexcluded()

Generate sub data frames for excluded and non-excluded points

GetXyAxis()

Retrieves the names of the x and y axis variables.

SeparateContDiscPlots()

Generate sub lists of plots with discrete and conitnuous z axis variables.

createIndexHtml()

Fills the index.html file with links to the interactive plots.

fillHover()

Generates the text of the hover, according to users’s requests.

makeContinuousPlots(data_frame, data_selection)

Generate plots with continuous z axis variables, using all data points

makeDataFrame()

Transform the main dictionary in a data frame.

makeDiscretePlots(data_frame, data_selection)

Generate plots with discrete z axis variables, using all data points

makePlots(indexfile)

Uses the data in self.data_dict to produce the plots.

Parameters:outFolder – Path to the output folder.

plotDescription()

Generate a description for each plot.

refiningVariableNames()

Redifining the output variable names to html format

tools.interactivePlotsHelpers.getEntry(inputDict, *keys)

Get entry key in dictionary inputDict. If a list of keys is provided, it will assumed nested dictionaries (e.g. key1,key2 will return inputDict[key1][key2]).

tools.interactivePlotsHelpers.getSlhaData(slhaFile)

Uses pyslha to read the SLHA file. Return a pyslha.Doc objec, if successful.

tools.interactivePlotsHelpers.getSlhaFile(smodelsOutput)

Returns the file name of the SLHA file corresponding to the output in smodelsDict

tools.interactivePlotsHelpers.importPythonOutput(smodelsFile)

Imports the smodels output from each .py file.

tools.interactivePlotsHelpers.outputStatus(smodelsDict)

Check the smodels output status in the file, if it’s -1, it will append ‘none’ to each list in the dictionary.

tools.ioObjects module

class tools.ioObjects.FileStatus

Bases: object

Object to run several checks on the input file. It holds an LheStatus (SlhaStatus) object if inputType = lhe (slha)

checkFile(inputFile)

Run checks on the input file.

Parameters:inputFile – path to input file

class tools.ioObjects.LheStatus(filename)

Bases: object

Object to check if input lhe file contains errors.

Variables:filename – path to input LHE file

evaluateStatus()

run status check

class tools.ioObjects.OutputStatus(status, inputFile, parameters, databaseVersion)

Bases: object

Object that holds all status information and has a predefined printout.

Initialize output. If one of the checks failed, exit.

Parameters:

• status – status of input file
• inputFile – input file name
• parameters – input parameters
• databaseVersion – database version (string)

addWarning(warning)

Append warning to warnings.

Parameters:warning – warning to be appended

updateSLHAStatus(status)

Update SLHA status.

Parameters:status – new SLHA status flag

updateStatus(status)

Update status.

Parameters:status – new status flag

class tools.ioObjects.SlhaStatus(filename, findMissingDecayBlocks=True, findIllegalDecays=False, checkXsec=True)

Bases: object

An instance of this class represents the status of an SLHA file. The output status is: = 0 : the file is not checked, = 1: the check is ok = -1: case of a physical problem, e.g. charged LSP, = -2: case of formal problems, e.g. no cross sections

Parameters:

• filename – path to input SLHA file
• findMissingDecayBlocks – if True add a warning for missing decay blocks
• findIllegalDecays – if True check if all decays are kinematically allowed
• checkXsec – if True check if SLHA file contains cross sections
• findLonglived – if True find stable charged particles and displaced vertices

emptyDecay(pid)

Check if any decay is missing for the particle with pid

Parameters:pid – PID number of particle to be checked Returns:True if the decay block is missing or if it is empty, None otherwise

evaluateStatus()

Get status summary from all performed checks.

Returns:a status flag and a message for explanation

findIllegalDecay(findIllegal)

Find decays for which the sum of daughter masses excels the mother mass

Parameters:findIllegal – True if check should be run Returns:status flag and message

findMissingDecayBlocks(findMissingBlocks)

For all non-SMpdgs particles listed in mass block, check if decay block is written

Returns:status flag and message

hasXsec(checkXsec)

Check if XSECTION table is present in the slha file.

Parameters:checkXsec – set True to run the check Returns:status flag, message

read()

Get pyslha output object.

tools.lheChecks module

tools.lheChecks.main(args)

tools.modelTester module

tools.modelTester.checkForSemicolon(strng, section, var)

tools.modelTester.getAllInputFiles(inFile)

Given inFile, return list of all input files

Parameters:inFile – Path to input file or directory containing input files Returns:List of all input files, and the directory name

tools.modelTester.getParameters(parameterFile)

Read parameter file, exit in case of errors

Parameters:parameterFile – Path to parameter File Returns:ConfigParser read from parameterFile

tools.modelTester.loadDatabase(parser, db)

Load database

Parameters:

• parser – ConfigParser with path to database
• db – binary database object. If None, then database is loaded, according to databasePath. If True, then database is loaded, and text mode is forced.

Returns:

database object, database version

tools.modelTester.loadDatabaseResults(parser, database)

Load database entries specified in parser

Parameters:

• parser – ConfigParser, containing analysis and txnames selection
• database – Database object

Returns:

List of experimental results

tools.modelTester.runSetOfFiles(inputFiles, outputDir, parser, databaseVersion, listOfExpRes, timeout, development, parameterFile)

Loop over all input files in inputFiles with testPoint

Parameters:

• inputFiles – list of input files to be tested
• outputDir – path to directory where output is be stored
• parser – ConfigParser storing information from parameter.ini file
• databaseVersion – Database version (printed to output file)
• listOfExpRes – list of ExpResult objects to be considered
• development – turn on development mode (e.g. no crash report)
• parameterFile – parameter file, for crash reports

Returns:

printers output

tools.modelTester.runSingleFile(inputFile, outputDir, parser, databaseVersion, listOfExpRes, timeout, development, parameterFile)

Call testPoint on inputFile, write crash report in case of problems

Parameters:

• inputFile – path to input file
• outputDir – path to directory where output is be stored
• parser – ConfigParser storing information from parameter.ini file
• databaseVersion – Database version (printed to output file)
• listOfExpRes – list of ExpResult objects to be considered
• crashReport – if True, write crash report in case of problems
• timeout – set a timeout for one model point (0 means no timeout)

Returns:

output of printers

tools.modelTester.setExperimentalFlag(parser)

set the experimental flag, if options:experimental = True

tools.modelTester.testPoint(inputFile, outputDir, parser, databaseVersion, listOfExpRes)

Test model point defined in input file (running decomposition, check results, test coverage)

Parameters:

• inputFile – path to input file
• outputDir – path to directory where output is be stored
• parser – ConfigParser storing information from parameters file
• databaseVersion – Database version (printed to output file)
• listOfExpRes – list of ExpResult objects to be considered

Returns:

output of printers

tools.modelTester.testPoints(fileList, inDir, outputDir, parser, databaseVersion, listOfExpRes, timeout, development, parameterFile)

Loop over all input files in fileList with testPoint, using ncpus CPUs defined in parser

Parameters:

• fileList – list of input files to be tested
• inDir – path to directory where input files are stored
• outputDir – path to directory where output is stored
• parser – ConfigParser storing information from parameter.ini file
• databaseVersion – Database version (printed to output files)
• listOfExpRes – list of ExpResult objects to be considered
• timeout – set a timeout for one model point (0 means no timeout)
• development – turn on development mode (e.g. no crash report)
• parameterFile – parameter file, for crash reports

Returns:

printer(s) output, if not run in parallel mode

tools.nllFastWrapper module

class tools.nllFastWrapper.NllFastWrapper(sqrts, nllfastVersion, testParams, testCondition)

Bases: smodels.tools.wrapperBase.WrapperBase

An instance of this class represents the installation of nllfast.

Parameters:

• sqrts – sqrt of s, in TeV, as an integer,
• nllfastVersion – version of the nllfast tool
• testParams – what are the test params we need to run things with?
• testCondition – the line that should be the last output line when running executable

SrcPath:

the path of the source code, for compilation

getKfactorsFor(pIDs, slhafile, pdf='cteq')

Read the NLLfast grid and returns a pair of k-factors (NLO and NLL) for the PIDs pair.

Returns:k-factors = None, if NLLfast does not contain the process; uses the slhafile to obtain the SUSY spectrum.

class tools.nllFastWrapper.NllFastWrapper13

Bases: tools.nllFastWrapper.NllFastWrapper

An instance of this class represents the installation of nllfast 8.

class tools.nllFastWrapper.NllFastWrapper7

Bases: tools.nllFastWrapper.NllFastWrapper

An instance of this class represents the installation of nllfast 7.

class tools.nllFastWrapper.NllFastWrapper8

Bases: tools.nllFastWrapper.NllFastWrapper

An instance of this class represents the installation of nllfast 8.

tools.physicsUnits module

tools.printer module

class tools.printer.BasicPrinter(output, filename)

Bases: object

Super class to handle the basic printing methods

addObj(obj)

Adds object to the Printer.

Parameters:obj – A object to be printed. Must match one of the types defined in formatObj Returns:True if the object has been added to the output. If the object does not belong to the pre-defined printing list toPrint, returns False.

filename

flush()

Format the objects added to the output, print them to the screen or file and remove them from the printer.

mkdir()

create directory to file, if necessary

openOutFile(filename, mode)

creates and opens a data sink, creates path if needed

setOptions(options)

Store the printer specific options to control the output of each printer. Each option is stored as a printer attribute.

Parameters:options – a list of (option,value) for the printer.

class tools.printer.MPrinter

Bases: object

Master Printer class to handle the Printers (one printer/output type)

addObj(obj)

Adds the object to all its Printers:

Parameters:obj – An object which can be handled by the Printers.

flush()

Ask all printers to write the output and clear their cache. If the printers return anything other than None, we pass it on.

setOutPutFiles(filename, silent=False)

Set the basename for the output files. Each printer will use this file name appended of the respective extension (i.e. .py for a python printer, .smodels for a summary printer,…)

Parameters:

• filename – Input file name
• silent – dont comment removing old files

setPrinterOptions(parser)

Define the printer types and their options.

Parameters:parser – ConfigParser storing information from the parameters file

class tools.printer.PyPrinter(output='stdout', filename=None)

Bases: tools.printer.BasicPrinter

Printer class to handle the printing of one single pythonic output

flush()

Write the python dictionaries generated by the object formatting to the defined output

setOutPutFile(filename, overwrite=True, silent=False)

Set the basename for the text printer. The output filename will be filename.py. :param filename: Base filename :param overwrite: If True and the file already exists, it will be removed. :param silent: dont comment removing old files

class tools.printer.SLHAPrinter(output='file', filename=None)

Bases: tools.printer.TxTPrinter

Printer class to handle the printing of slha format summary output. It uses the facilities of the TxTPrinter.

setOutPutFile(filename, overwrite=True, silent=False)

Set the basename for the text printer. The output filename will be filename.smodels. :param filename: Base filename :param overwrite: If True and the file already exists, it will be removed. :param silent: dont comment removing old files

class tools.printer.SummaryPrinter(output='stdout', filename=None)

Bases: tools.printer.TxTPrinter

Printer class to handle the printing of one single summary output. It uses the facilities of the TxTPrinter.

setOutPutFile(filename, overwrite=True, silent=False)

Set the basename for the text printer. The output filename will be filename.smodels. :param filename: Base filename :param overwrite: If True and the file already exists, it will be removed. :param silent: dont comment removing old files

class tools.printer.TxTPrinter(output='stdout', filename=None)

Bases: tools.printer.BasicPrinter

Printer class to handle the printing of one single text output

setOutPutFile(filename, overwrite=True, silent=False)

Set the basename for the text printer. The output filename will be filename.log.

Parameters:

• filename – Base filename
• overwrite – If True and the file already exists, it will be removed.
• silent – dont comment removing old files

class tools.printer.XmlPrinter(output='stdout', filename=None)

Bases: tools.printer.PyPrinter

Printer class to handle the printing of one single XML output

convertToElement(pyObj, parent, tag='')

Convert a python object (list,dict,string,…) to a nested XML element tree. :param pyObj: python object (list,dict,string…) :param parent: XML Element parent :param tag: tag for the daughter element

flush()

Get the python dictionaries generated by the object formatting to the defined output and convert to XML

setOutPutFile(filename, overwrite=True, silent=False)

Set the basename for the text printer. The output filename will be filename.xml. :param filename: Base filename :param overwrite: If True and the file already exists, it will be removed. :param silent: dont comment removing old files

tools.printer.getInfoFromPython(output)

Retrieves information from the python output

Parameters:output – output (dictionary) Returns:list of r-values,r-expected and analysis IDs. None if no results are found.

tools.printer.getInfoFromSLHA(output)

Retrieves information from the SLHA output

Parameters:output – output (string) Returns:list of r-values,r-expected and analysis IDs. None if no results are found.

tools.printer.getInfoFromSummary(output)

Retrieves information from the summary output

Parameters:output – output (string) Returns:list of r-values,r-expected and analysis IDs. None if no results are found.

tools.printer.getSummaryFrom(output, ptype)

Retrieves information about the output according to the printer type (slha,python or summary)

Parameters:

• output – output (dictionary for ptype=python or string for ptype=slha/summary)
• ptype – Printer type (slha, python or summary)

Returns:

Dictionary with the output information

tools.printer.printScanSummary(outputDict, outputFile)

Method for creating a simple summary of the results when running SModelS over multiple files.

Parameters:

• outputDict – A dictionary with filenames as keys and the master printer flush dictionary as values.
• outputFile – Path to the summary file to be written.

tools.proxyDBCreator module

class tools.proxyDBCreator.ProxyDBCreater(inputfile, rundir, verbose='info')

Bases: object

create(servername, serverport)

pprint(*args)

run(really)

now run the server :param really: if False, then only write out command

store(outputfile)

store the outputfile

symlink()

set a symlink from self.outputfile to default.pcl

tools.proxyDBCreator.main(args)

tools.pyhfInterface module

class tools.pyhfInterface.PyhfData(nsignals, inputJsons, jsonFiles=None)

Bases: object

Holds data for use in pyhf :ivar nsignals: signal predictions list divided into sublists, one for each json file :ivar inputJsons: list of json instances :ivar jsonFiles: optional list of json files :ivar nWS: number of workspaces = number of json files

checkConsistency()

Check various inconsistencies of the PyhfData attributes

Parameters:zeroSignalsFlag – boolean identifying if all SRs of a single json are empty

getWSInfo()

Getting informations from the json files

Variables:channelsInfo – list of dictionaries (one dictionary for each json file) containing useful information about the json files - :key signalRegions: list of dictonaries with ‘json path’ and ‘size’ (number of bins) of the ‘signal regions’ channels in the json files - :key otherRegions: list of strings indicating the path to the control and validation region channels

class tools.pyhfInterface.PyhfUpperLimitComputer(data, cl=0.95)

Bases: object

Class that computes the upper limit using the jsons files and signal informations in the data instance of PyhfData

Parameters:

• data – instance of PyhfData holding the signals information
• cl – confdence level at which the upper limit is desired to be computed

Variables:

• data – created from :param data:
• nsignals – signal predictions list divided into sublists, one for each json file
• inputJsons – list of input json files as python json instances
• channelsInfo – list of channels information for the json files
• zeroSignalsFlag – list boolean flags in case all signals are zero for a specific json
• nWS – number of workspaces = number of json files
• patches – list of patches to be applied to the inputJsons as python dictionary instances
• workspaces – list of workspaces resulting from the patched inputJsons
• cl – created from :param cl:
• scale – scale that is applied to the signal predictions, dynamically changes throughout the upper limit calculation
• alreadyBeenThere – boolean flag that identifies when the :ivar nsignals: accidentally passes twice at two identical values

checkPyhfVersion()

check the pyhf version, currently we need 0.6.1+

chi2(workspace_index=None)

Returns the chi square

exponentiateNLL(twice_nll, doIt)

if doIt, then compute likelihood from nll, else return nll

getBestCombinationIndex()

find the index of the best expected combination

likelihood(workspace_index=None, nll=False)

Returns the value of the likelihood. Inspired by the pyhf.infer.mle module but for non-log likelihood :param workspace_index: supply index of workspace to use. If None,

choose index of best combo

Parameters:nll – if true, return nll, not llhd

lmax(workspace_index=None, nll=False)

Returns the negative log max likelihood :param nll: if true, return nll, not llhd :param workspace_index: supply index of workspace to use. If None,

choose index of best combo

patchMaker()

Method that creates the list of patches to be applied to the self.inputJsons workspaces, one for each region given the self.nsignals and the informations available in self.channelsInfo and the content of the self.inputJsons NB: It seems we need to include the change of the “modifiers” in the patches as well

Returns:the list of patches, one for each workspace

rescale(factor)

Rescales the signal predictions (self.nsignals) and processes again the patches and workspaces

Returns:updated list of patches and workspaces (self.patches and self.workspaces)

ulSigma(expected=False, workspace_index=None)

Compute the upper limit on the signal strength modifier with:

• by default, the combination of the workspaces contained into self.workspaces
• if workspace_index is specified, self.workspace[workspace_index] (useful for computation of the best upper limit)

Parameters:

• expected –
  • if set to True: uses expected SM backgrounds as signals
  • else: uses self.nsignals
• workspace_index –
  • if different from None: index of the workspace to use for upper limit
  • else: choose best combo

Returns:

the upper limit at self.cl level (0.95 by default)

welcome()

greet the world

wsMaker()

Apply each region patch (self.patches) to his associated json (self.inputJsons) to obtain the complete workspaces

Returns:the list of patched workspaces

tools.pyhfInterface.getLogger()

Configure the logging facility. Maybe adapted to fit into your framework.

tools.pythia6Wrapper module

class tools.pythia6Wrapper.Pythia6Wrapper(configFile='<install>/smodels/etc/pythia.card', executablePath='<install>/smodels/lib/pythia6/pythia_lhe', srcPath='<install>/smodels/lib/pythia6/')

Bases: smodels.tools.wrapperBase.WrapperBase

An instance of this class represents the installation of pythia6. nevents keeps track of how many events we run. For each event we only allow a certain computation time: if self.secondsPerEvent * self.nevents > CPU time, we terminate Pythia.

Parameters:

• configFile – Location of the config file, full path; copy this file and provide tools to change its content and to provide a template
• executablePath – Location of executable, full path (pythia_lhe)
• srcPath – Location of source code

checkFileExists(inputFile)

Check if file exists, raise an IOError if it does not.

Returns:absolute file name if file exists.

replaceInCfgFile(replacements={'NEVENTS': 10000, 'SQRTS': 8000})

Replace strings in the config file by other strings, similar to setParameter.

This is introduced as a simple mechanism to make changes to the parameter file.

Parameters:replacements – dictionary of strings and values; the strings will be replaced with the values; the dictionary keys must be strings present in the config file

run(slhafile, lhefile=None, unlink=True)

Execute pythia_lhe with n events, at sqrt(s)=sqrts.

Parameters:

• slhafile – input SLHA file
• lhefile – option to write LHE output to file; if None, do not write output to disk. If lhe file exists, use its events for xsecs calculation.
• unlink – Clean up temp directory after running pythia

Returns:

List of cross sections

setParameter(param='MSTP(163)', value=6)

Modifies the config file, similar to .replaceInCfgFile.

It will set param to value, overwriting possible old values.

unlink(unlinkdir=True)

Remove temporary files.

Parameters:unlinkdir – remove temp directory completely

tools.pythia8Wrapper module

class tools.pythia8Wrapper.Pythia8Wrapper(configFile='<install>/smodels/etc/pythia8.cfg', executablePath='<install>/smodels/lib/pythia8/pythia8.exe', srcPath='<install>/smodels/lib/pythia8/')

Bases: smodels.tools.wrapperBase.WrapperBase

An instance of this class represents the installation of pythia8.

Parameters:

• configFile – Location of the config file, full path; copy this file and provide tools to change its content and to provide a template
• executablePath – Location of executable, full path (pythia8.exe)
• srcPath – Location of source code

checkFileExists(inputFile)

Check if file exists, raise an IOError if it does not.

Returns:absolute file name if file exists.

chmod()

chmod 755 on pythia executable, if it exists. Do nothing, if it doesnt exist.

run(slhaFile, lhefile=None, unlink=True)

Run pythia8.

Parameters:

• slhaFile – SLHA file
• lhefile – option to write LHE output to file; if None, do not write output to disk. If lhe file exists, use its events for xsecs calculation.
• unlink – clean up temporary files after run?

Returns:

List of cross sections

unlink(unlinkdir=True)

Remove temporary files.

Parameters:unlinkdir – remove temp directory completely

tools.pythia8particles module

tools.reweighting module

tools.reweighting.calculateProbabilities(width, Leff_inner, Leff_outer)

The fraction of prompt and displaced decays are defined as:

F_long = exp(-totalwidth*l_outer/gb_outer) F_prompt = 1 - exp(-totaltotalwidth*l_inner/gb_inner) F_displaced = 1 - F_prompt - F_long

Parameters:

• Leff_inner – is the effective inner radius of the detector, given in meters
• Leff_outer – is the effective outer radius of the detector, given in meters
• width – particle width for which probabilities should be calculated (in GeV)

Returns:

Dictionary with the probabilities for the particle not to decay (in the detector), to decay promptly or displaced.

tools.reweighting.defaultEffReweight(element, Leff_inner=None, Leff_outer=None, minWeight=1e-10)

Computes the lifetime reweighting factor for the element efficiency based on the lifetimes of all intermediate particles and the last stable odd-particle appearing in the element. The fraction corresponds to the fraction of decays corresponding to prompt decays to all intermediate BSM particles and to a long-lived decay (outside the detector) to the final BSM state.

Parameters:

• element – Element object or nested list of widths
• minWeight – Lower cut for the reweighting factor. Any value below this will be taken to be zero.
• Leff_inner – is the effective inner radius of the detector, given in meters. If None, use default value.
• Leff_outer – is the effective outer radius of the detector, given in meters. If None, use default value.

Returns:

Reweight factor (float)

tools.reweighting.defaultULReweight(element, Leff_inner=None, Leff_outer=None)

Computes the lifetime reweighting factor for the element upper limit based on the lifetimes of all intermediate particles and the last stable odd-particle appearing in the element. The fraction corresponds to the fraction of decays corresponding to prompt decays to all intermediate BSM particles and to a long-lived decay (outside the detector) to the final BSM state.

Parameters:

• element – Element object
• Leff_inner – is the effective inner radius of the detector, given in meters. If None, use default value.
• Leff_outer – is the effective outer radius of the detector, given in meters. If None, use default value.

Returns:

Reweight factor (float)

tools.reweighting.reweightFactorFor(element, resType='prompt', Leff_inner=None, Leff_outer=None)

Computer the reweighting factor for the element according to the experimental result type. Currently only two result types are supported: ‘prompt’ and ‘displaced’. If resultType = ‘prompt’, returns the reweighting factor for all decays in the element to be prompt and the last odd particle to be stable. If resultType = ‘displaced’, returns the reweighting factor for ANY decay in the element to be displaced and no long-lived decays and the last odd particle to be stable. Not that the fraction of “long-lived (meta-stable) decays” is usually included in topologies where the meta-stable particle appears in the final state. Hence it should not be included in the prompt or displaced fractions.

Parameters:

• element – Element object
• resType – Type of result to compute the reweight factor for (either ‘prompt’ or ‘displaced’)
• Leff_inner – is the effective inner radius of the detector, given in meters. If None, use default value.
• Leff_outer – is the effective outer radius of the detector, given in meters. If None, use default value.

Returns:

probabilities (depending on types of decay within branch), branches (with different labels depending on type of decay)

tools.runSModelS module

tools.runSModelS.main()

tools.runSModelS.run(inFile, parameterFile, outputDir, db, timeout, development)

Provides a command line interface to basic SModelS functionalities.

Parameters:

• inFile – input file name (either a SLHA or LHE file) or directory name (path to directory containing input files)
• parameterFile – File containing the input parameters (default = smodels/etc/parameters_default.ini)
• outputDir – Output directory to write a summary of results to
• db – supply a smodels.experiment.databaseObj.Database object, so the database doesn’t have to be loaded anymore. Will render a few parameters in the parameter file irrelevant. If None, load the database as described in parameterFile, If True, force loading the text database.
• timeout – set a timeout for one model point (0 means no timeout)
• development – turn on development mode (e.g. no crash report)

tools.runtime module

tools.runtime.experimentalFeatures()

a simple boolean flag to turn experimental features on/off, can be turned on and off via options:experimental in parameters.ini.

tools.runtime.filetype(filename)

obtain information about the filetype of an input file, currently only used to discriminate between slha and lhe files.

Returns:filetype as string(“slha” or “lhe”), None if file does not exist, or filetype is unknown.

tools.runtime.nCPUs()

obtain the number of CPU cores on the machine, for several platforms and python versions.

tools.simplifiedLikelihoods module

class tools.simplifiedLikelihoods.Data(observed, backgrounds, covariance, third_moment=None, nsignal=None, name='model', deltas_rel=0.2)

Bases: object

A very simple observed container to collect all the data needed to fully define a specific statistical model

Parameters:

• observed – number of observed events per dataset
• backgrounds – expected bg per dataset
• covariance – uncertainty in background, as a covariance matrix
• nsignal – number of signal events in each dataset
• name – give the model a name, just for convenience
• deltas_rel – the assumed relative error on the signal hypotheses. The default is 20%.

convert(obj)

Convert object to numpy arrays. If object is a float or int, it is converted to a one element array.

correlations()

Correlation matrix, computed from covariance matrix. Convenience function.

diagCov()

Diagonal elements of covariance matrix. Convenience function.

isLinear()

Statistical model is linear, i.e. no quadratic term in poissonians

isScalar(obj)

Determine if obj is a scalar (float or int)

sandwich()

Sandwich product

signals(mu)

Returns the number of expected signal events, for all datasets, given total signal strength mu.

Parameters:mu – Total number of signal events summed over all datasets.

totalCovariance(nsig)

get the total covariance matrix, taking into account also signal uncertainty for the signal hypothesis <nsig>. If nsig is None, the predefined signal hypothesis is taken.

var_s(nsig=None)

The signal variances. Convenience function.

Parameters:nsig – If None, it will use the model expected number of signal events, otherwise will return the variances for the input value using the relative signal uncertainty defined for the model.

zeroSignal()

Is the total number of signal events zero?

class tools.simplifiedLikelihoods.LikelihoodComputer(data, ntoys=30000)

Bases: object

Parameters:

• data – a Data object.
• ntoys – number of toys when marginalizing

chi2(nsig, marginalize=False)

Computes the chi2 for a given number of observed events nobs given the predicted background nb, error on this background deltab, expected number of signal events nsig and the relative error on signal (deltas_rel). :param marginalize: if true, marginalize, if false, profile :param nsig: number of signal events :return: chi2 (float)

dLdMu(mu, signal_rel, theta_hat)

d (ln L)/d mu, if L is the likelihood. The function whose root gives us muhat, i.e. the mu that maximizes the likelihood.

Parameters:

• mu – total number of signal events
• signal_rel – array with the relative signal strengths for each dataset (signal region)
• theta_hat – array with nuisance parameters

debug_mode = False

findMuHat(signal_rel, allowNegativeSignals=False)

Find the most likely signal strength mu given the relative signal strengths in each dataset (signal region).

Parameters:

• signal_rel – array with relative signal strengths
• allowNegativeSignals – if true, then also allow for negative values

Returns:

mu_hat, the maximum likelihood estimate of mu

findThetaHat(nsig)

Compute nuisance parameter theta that maximizes our likelihood (poisson*gauss).

getSigmaMu(signal_rel)

Get a rough estimate for the variance of mu around mu_max.

Parameters:signal_rel – array with relative signal strengths in each dataset (signal region)

getThetaHat(nobs, nb, nsig, covb, max_iterations)

Compute nuisance parameter theta that maximizes our likelihood (poisson*gauss).

likelihood(nsig, marginalize=False, nll=False)

compute likelihood for nsig, profiling the nuisances :param marginalize: if true, marginalize, if false, profile :param nll: return nll instead of likelihood

lmax(nsig=None, marginalize=False, nll=False, allowNegativeSignals=False)

convenience function, computes likelihood for nsig = nobs-nbg, :param marginalize: if true, marginalize, if false, profile nuisances. :param nsig: number of signal events, needed only for combinations

if None, then it gets replaced with obsN - expBG

Parameters:

• nll – return nll instead of likelihood
• allowNegativeSignals – if False, then negative nsigs are replaced with 0.

marginalizedLLHD1D(nsig, nll)

Return the likelihood (of 1 signal region) to observe nobs events given the predicted background nb, error on this background (deltab), expected number of signal events nsig and the relative error on the signal (deltas_rel).

Parameters:

• nsig – predicted signal (float)
• nobs – number of observed events (float)
• nb – predicted background (float)
• deltab – uncertainty on background (float)

Returns:

likelihood to observe nobs events (float)

marginalizedLikelihood(nsig, nll)

compute the marginalized likelihood of observing nsig signal event

nll(theta)

probability, for nuicance parameters theta, as a negative log likelihood.

nllHess(theta)

the Hessian of nll as a function of the thetas. Makes it easier to find the maximum likelihood.

nllprime(theta)

the derivative of nll as a function of the thetas. Makes it easier to find the maximum likelihood.

probMV(nll, theta)

probability, for nuicance parameters theta :params nll: if True, compute negative log likelihood

profileLikelihood(nsig, nll)

compute the profiled likelihood for nsig. Warning: not normalized. Returns profile likelihood and error code (0=no error)

class tools.simplifiedLikelihoods.UpperLimitComputer(ntoys=30000, cl=0.95)

Bases: object

Parameters:

• ntoys – number of toys when marginalizing
• cl – desired quantile for limits

debug_mode = False

ulSigma(model, marginalize=False, toys=None, expected=False, trylasttime=False)

upper limit obtained from the defined Data (using the signal prediction

for each signal regio/dataset), by using the q_mu test statistic from the CCGV paper (arXiv:1007.1727).

Params marginalize:  if true, marginalize nuisances, else profile them Params toys:specify number of toys. Use default is none Params expected:  compute the expected value, not the observed. Params trylasttime:  if True, then dont try extra Returns:upper limit on yields

tools.simplifiedLikelihoods.getLogger()

Configure the logging facility. Maybe adapted to fit into your framework.

tools.slhaChecks module

tools.slhaChecks.main(args)

tools.smodelsLogging module

class tools.smodelsLogging.ColorizedStreamHandler(stream=None)

Bases: logging.StreamHandler

format(record)

Format the specified record.

If a formatter is set, use it. Otherwise, use the default formatter for the module.

should_color()

tools.smodelsLogging.getLogLevel(asString=False)

obtain the current log level. :params asString: return string, not number.

tools.smodelsLogging.getLogger()

tools.smodelsLogging.setLogLevel(level)

set the log level of the central logger. can either be directly an integer ( e.g. logging.DEBUG ), or “debug”, “info”, “warning”, or “error”.

tools.smodelsTools module

tools.smodelsTools.main()

tools.statistics module

tools.statistics.chi2FromLimits(likelihood, upperLimit, expectedUpperLimit, corr=0.0)

compute the chi2 value from a likelihood (convenience function).

tools.statistics.deltaChi2FromLlhd(likelihood)

compute the delta chi2 value from a likelihood (convenience function)

tools.statistics.likelihoodFromLimits(upperLimit, expectedUpperLimit, nsig, nll=False, allowNegativeMuhat=True, corr=0.6)

computes the likelihood from an expected and an observed upper limit. :param upperLimit: observed upper limit, as a yield (i.e. unitless) :param expectedUpperLimit: expected upper limit, also as a yield :param nsig: number of signal events, if None then nsig = mumax :param nll: if True, return negative log likelihood :param allowNegativeMuhat: if True, then allow muhat to become negative,

else demand that muhat >= 0. In the presence of underfluctuations in the data, setting this to True results in more realistic approximate likelihoods.

Parameters:corr – correction factor: ULexp_mod = ULexp / (1. - corr*((ULobs-ULexp)/(ULobs+ULexp))) When comparing with likelihoods constructed from efficiency maps, a factor of corr = 0.6 has been found to result in the best approximations. Returns:likelihood (float)

tools.statistics.rvsFromLimits(upperLimit, expectedUpperLimit, n=1, corr=0.0)

Generates a sample of random variates, given expected and observed likelihoods. The likelihood is modelled as a truncated Gaussian.

Parameters:

• upperLimit – observed upper limit, as a yield (i.e. unitless)
• expectedUpperLimit – expected upper limit, also as a yield
• n – sample size
• corr – correction term

Returns:

sample of random variates

tools.stringTools module

tools.stringTools.cleanWalk(topdir)

perform os.walk, but ignore all hidden files and directories

tools.stringTools.concatenateLines(oldcontent)

of all lines in the list “oldcontent”, concatenate the ones that end with or ,

tools.timeOut module

exception tools.timeOut.NoTime(value=None)

Bases: Exception

The time out exception. Raised when the running time exceeds timeout

class tools.timeOut.Timeout(sec)

Bases: object

Timeout class using ALARM signal.

raise_timeout(*args)

tools.toolBox module

class tools.toolBox.ToolBox

Bases: object

A singleton-like class that keeps track of all external tools. Intended to make installation and deployment easier.

Constructor creates the singleton.

add(instance)

Adds a tool by passing an instance to this method.

checkInstallation(make=False, printit=True, longL=False)

Checks if all tools listed are installed properly, returns True if everything is ok, False otherwise.

compile()

Tries to compile and install tools that are not yet marked as ‘installed’.

get(tool, verbose=True)

Gets instance of tool from the toolbox.

initSingleton()

Initializes singleton instance (done only once for the entire class).

installationOk(ok)

Returns color coded string to signal installation issues.

listOfTools()

Returns a simple list with the tool names.

tools.toolBox.main(args)

tools.wrapperBase module

class tools.wrapperBase.WrapperBase

Bases: object

An instance of this class represents the installation of an external tool.

An external tool encapsulates a tool that is executed via commands.getoutput. The wrapper defines how the tool is tested for proper installation and how the tool is executed.

absPath(path)

Get the absolute path of <path>, replacing <install> with the installation directory.

basePath()

Get the base installation path.

checkInstallation(compile=True)

Checks if installation of tool is correct by looking for executable and executing it. If check is False and compile is True, then try and compile it.

Returns:True, if everything is ok

chmod()

chmod 755 on executable, if it exists. Do nothing, if it doesnt exist.

compile()

Try to compile the tool.

complain()

defaulttempdir = '/tmp/'

installDirectory()

Returns:the installation directory of the tool

pathOfExecutable()

Returns:path of executable

tempDirectory()

Return the temporary directory name.

tools.wrapperBase.ok(b)

Returns:‘ok’ if b is True, else, return ‘error’.

tools.xsecComputer module

class tools.xsecComputer.ArgsStandardizer

Bases: object

simple class to collect all argument manipulators

checkAllowedSqrtses(order, sqrtses)

check if the sqrtses are ‘allowed’

checkNCPUs(ncpus, inputFiles)

getInputFiles(args)

geth the names of the slha files to run over

getOrder(args)

retrieve the order in perturbation theory from argument list

getPythiaVersion(args)

getSSMultipliers(multipliers)

getSqrtses(args)

extract the sqrtses from argument list

queryCrossSections(filename)

writeToFile(args)

class tools.xsecComputer.XSecComputer(maxOrder, nevents, pythiaVersion, maycompile=True)

Bases: object

cross section computer class, what else?

Parameters:

• maxOrder – maximum order to compute the cross section, given as an integer if maxOrder == LO, compute only LO pythia xsecs if maxOrder == NLO, apply NLO K-factors from NLLfast (if available) if maxOrder == NLL, apply NLO+NLL K-factors from NLLfast (if available)
• nevents – number of events for pythia run
• pythiaVersion – pythia6 or pythia8 (integer)
• maycompile – if True, then tools can get compiled on-the-fly

addCommentToFile(comment, slhaFile)

add the optional comment to file

addHigherOrders(sqrts, slhafile)

add higher order xsecs

addMultipliersToFile(ssmultipliers, slhaFile)

add the signal strength multipliers to the SLHA file

addXSecToFile(xsecs, slhafile, comment=None, complain=True)

Write cross sections to an SLHA file.

Parameters:

• xsecs – a XSectionList object containing the cross sections
• slhafile – target file for writing the cross sections in SLHA format
• comment – optional comment to be added to each cross section block
• complain – complain if there are already cross sections in file

applyMultipliers(xsecs, ssmultipliers)

apply the given multipliers to the cross sections

compute(sqrts, slhafile, lhefile=None, unlink=True, loFromSlha=None, pythiacard=None, ssmultipliers=None)

Run pythia and compute SUSY cross sections for the input SLHA file.

Parameters:

• sqrts – sqrt{s} to run Pythia, given as a unum (e.g. 7.*TeV)
• slhafile – SLHA file
• lhefile – LHE file. If None, do not write pythia output to file. If file does not exist, write pythia output to this file name. If file exists, read LO xsecs from this file (does not run pythia).
• unlink – Clean up temp directory after running pythia
• loFromSlha – If True, uses the LO xsecs from the SLHA file to compute the higher order xsecs
• pythiaCard – Optional path to pythia.card. If None, uses smodels/etc/pythia.card
• ssmultipliers – optionally supply signal strengh multipliers, given as dictionary of the tuple of the mothers’ pids as keys and multipliers as values, e.g { (1000001,1000021):1.1 }.

Returns:

XSectionList object

computeForBunch(sqrtses, inputFiles, unlink, lOfromSLHA, tofile, pythiacard=None, ssmultipliers=None)

compute xsecs for a bunch of slha files

computeForOneFile(sqrtses, inputFile, unlink, lOfromSLHA, tofile, pythiacard=None, ssmultipliers=None, comment=None)

Compute the cross sections for one file.

Parameters:

• sqrtses – list of sqrt{s} tu run pythia, as a unum (e.g. [7*TeV])
• inputFile – input SLHA file to compute xsecs for
• unlink – if False, keep temporary files
• lofromSLHA – try to obtain LO xsecs from SLHA file itself
• tofile – False, True, “all”: write results to file, if “all” also write lower xsecs to file.
• pythiacard – optionally supply your own runcard
• ssmultipliers – optionally supply signal strengh multipliers, given as dictionary of the tuple of the mothers’ pids as keys and multipliers as values, e.g { (1000001,1000021):1.1 }.
• comment – an optional comment that gets added to the slha file.

Returns:

number of xsections that have been computed

getPythia()

returns the pythia tool that is configured to be used

match(pids, theorypid)

do the pids given by the user match the pids of the theorypred?

xsecToBlock(xsec, inPDGs=(2212, 2212), comment=None, xsecUnit=1.00E+00 [pb])

Generate a string for a XSECTION block in the SLHA format from a XSection object.

Parameters:

• inPDGs – defines the PDGs of the incoming states (default = 2212,2212)
• comment – is added at the end of the header as a comment
• xsecUnit – unit of cross sections to be written (default is pb). Must be a Unum unit.

tools.xsecComputer.main(args)

Module contents