covarianceTool.py File Reference

Go to the source code of this file.

Namespaces
	covarianceTool

Functions
def	covarianceTool.isFloat (x)
def	covarianceTool.convertHistToScatter (ao)
def	covarianceTool.renameAOs (fin_name, aoMap)
def	covarianceTool.makeBeamer (plotDictionary, outdir)
def	covarianceTool.makeSummaryTable (plotDictionary, outfile, hdataName=None)
def	covarianceTool.matchAOs (aoList1, aoList2, strategy=0, recursive=True)
def	covarianceTool.markupYODAwithCorrelationsFromYODAs (reference, correlationsDir)
def	covarianceTool.markupYODAwithCorrelationsFromYAML (reference, correlationsDir)
def	covarianceTool.markupYODAwithCorrelations (ref, corrDir, ignoreCorrelations)
def	covarianceTool.returnFileContents (filename, inputType='data')
def	covarianceTool.getCorrelationInfoFromWeb (name=None, dlformat="yoda")

Variables
	covarianceTool.parser = optparse.OptionParser(usage="%prog [options]")
	covarianceTool.help
	covarianceTool.dest
	covarianceTool.default
	covarianceTool.False
	covarianceTool.action
	covarianceTool.opts
	covarianceTool.args
dictionary	covarianceTool.histograms
	covarianceTool.ntoys
	covarianceTool.verbosity
	covarianceTool.filter
	covarianceTool.ignore_corrs_data
	covarianceTool.ignore_corrs_mc
dictionary	covarianceTool.mcNames = {}
dictionary	covarianceTool.plotDictionary = {'error-analysis': {}, 'covariance-matrix': {}, 'correlation-matrix': {}, 'data-vs-mc': {}, 'chi2-contribs': {}, 'chi2-value': {}, 'covDetails': {}, 'summary-plot': {}, 'summary-table': {}}
	covarianceTool.data
string	covarianceTool.outdir = "outputs/%s/data/plots" % (opts.data.replace(".yoda", ""))
	covarianceTool.plotparser = rivet.mkStdPlotParser([], [])
	covarianceTool.headers = plotparser.getHeaders(hdata.path())
	covarianceTool.XLabel
	covarianceTool.Title
	covarianceTool.xLabel
	covarianceTool.title
	covarianceTool.mcName = mc.split(":")[1]
	covarianceTool.mc = mc.split(":")[0]
def	covarianceTool.mcnew = markupYODAwithCorrelations(mc, opts.corr_mc, opts.ignore_corrs_mc)
def	covarianceTool.aoMap = matchAOs(histograms['data'], histograms['models'][mcnew])
def	covarianceTool.newmc = renameAOs(mcnew, aoMap)
	covarianceTool.dataSuperAO = ct.makeSuperAO(histograms['data'])
	covarianceTool.mcSuperAO = ct.makeSuperAO(histograms['models'][mc])
dictionary	covarianceTool.mcResults = {}
bool	covarianceTool.passFilter = False
def	covarianceTool.hmc = aoMap[hdata]
string	covarianceTool.covDetailsData = "Size of uncertainties across range, and Data Correlation Matrix"
	covarianceTool.covData = ct.makeCovarianceMatrixFromToys(hdata, opts.ntoys, opts.ignore_corrs_data)
string	covarianceTool.covDetailsMC = "MC Covariance Matrix"
	covarianceTool.covMC = ct.makeCovarianceMatrixFromToys(hmc, opts.ntoys, opts.ignore_corrs_mc)
	covarianceTool.covTotal = covData + covMC
	covarianceTool.chi2
	covarianceTool.ndf
	covarianceTool.prob
	covarianceTool.chi2contribs
	covarianceTool.chi2ContribsByRow = ct.chi2ContribsByRow(chi2contribs)
	covarianceTool.chi2ContribsByRowYAML = yaml.dump(chi2ContribsByRow, default_flow_style=True, default_style='', width=1e6)
dictionary	covarianceTool.res = {'%s' % opts.data: '[Data]', '%s' % model: '[%s (# chi^2=%.2f/%d)]' % (mcName, chi2, ndf)}
string	covarianceTool.outdirplots = outdir + "/data-vs-%s/plots/" % mcName
	covarianceTool.plots = st.makeSystematicsPlotsWithROOT(res, outdirplots, nominalName='Data', ratioZoom=None, regexFilter=".*%s.*" % hmc.name(), regexVeto=None)
	covarianceTool.pathName = hdata.path().replace("/REF", "")
dictionary	covarianceTool.mcR = mcResults[model]
def	covarianceTool.beamerPath = makeBeamer(plotDictionary, outdir)