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

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

Detailed Description

The `covarianceTool.py` executable dis a helper which uses the functions provided by `covarianceToolsLibrary.py` to perform the
comparison between MC predictions and HEPData records.

However, the `local/bin/covarianceToolsLibrary.py` can also be used directly to manipulate covariance info and evaluate goodness of fit.
Examples of how to do this are provided in the `examples` subdirectory:
- `examples/covarianceTool-examples/README.md`

The first thing to do is test you can access the `covarianceTool.py` executable and see available options:
```
covarianceTool.py -h
```

Author: Louie D. Corpe (CERN)
Email: l.corpe@cern.ch

Function Documentation

◆ convertHistToScatter()

covarianceTool.convertHistToScatter ( ao )

Definition at line 69 of file covarianceTool.py.

def convertHistToScatter(ao):
  print("convertHistToScatter: This is a placeholder, need to implement")
  exit(1)
 
 

◆ getCorrelationInfoFromWeb()

covarianceTool.getCorrelationInfoFromWeb	(		name = None,
			dlformat = "yoda" )

Definition at line 479 of file covarianceTool.py.

def getCorrelationInfoFromWeb(name=None, dlformat="yoda"):
  # try to get the YAML files for the corresponding HEPData record
  # download and untar them...
  if (name is None): name = opts.data
  inspireID = [t for t in name.split("_") if "I" in t]
  inspireID = inspireID[0].replace("I", "")
  hdurl = "http://www.hepdata.net/record/ins%s?format=%s" % (inspireID, dlformat)
  print("[INFO] Trying to download information from %s", hdurl)
  if hdurl:
    response = urllib2.urlopen(hdurl)
    download = response.read()
    if not download or "<html" in download:
        print(("Problem encountered when getting data from HepData (%s). No reference data file written." % hdurl))
    else:
        tar = tarfile.open(mode="r:gz", fileobj=StringIO.StringIO(download))
        fnames = tar.getnames()
        if len(fnames) > 0 and dlformat == 'yaml':
            tar.extractall()
            os.system("mkdir -p corrInfo/ins%s" % (inspireID))
            os.system("mv %s/* corrInfo/ins%s/." % (fnames[0], inspireID))
            os.system("rm -r %s" % (fnames[0]))
            print("[INFO] successfully downloaded YAML files from HEPMC, will see if they can be used for correlations")
        if len(fnames) == 1 and dlformat == 'yoda':
            tar.extractall()
            os.system("mkdir -p corrInfo/ins%s" % (inspireID))
            os.system("mv %s corrInfo/ins%s/." % (fnames[0], inspireID))
            print("[INFO] successfully downloaded YODA files from HEPMC, will see if they can be used for correlations")
    opts.corr_data = "corrInfo/ins%s" % inspireID
    response.close()
 
 

◆ isFloat()

covarianceTool.isFloat ( x )

Definition at line 60 of file covarianceTool.py.

def isFloat(x):
  # check if the object can be cast as a float
  try:
    float(x)
    return True
  except ValueError:
    return False
 
 

◆ makeBeamer()

covarianceTool.makeBeamer	(		plotDictionary,
			outdir )

Definition at line 93 of file covarianceTool.py.

def makeBeamer(plotDictionary, outdir):
  # produce summary slides using beamer
  frames = []
  dataDir = os.environ["SYSTTOOLSPATH"] + "/data/"
  os.system("mkdir -p %s " % outdir)
  samples = plotDictionary['chi2-value'].keys()
  histos = plotDictionary['chi2-value'][samples[0]].keys()
  os.system('cp %s/title.tex %s/title.tex' % (dataDir, outdir))
  os.system('cp %s/title.tex %s/title.tex' % (dataDir, outdir))
  os.system("sed -i -e 's|!TITLE!|%s|g' %s/title.tex" % (opts.data.replace('.yoda', '').replace('_', ' '), outdir))
  frames.append("%s/title.tex" % outdir)
  for histo in histos:
     if 'superAO' in histo: continue
     os.system('cp %s/section.tex %s/%s_section.tex' % (dataDir, outdir, histo))
     os.system("sed -i -e 's|!AOREF!|%s|g' %s/%s_section.tex" % (histo, outdir, histo))
     frames.append('%s/%s_section.tex' % (outdir, histo))
     os.system('cp %s/errorAnalysis.tex %s/%s_%s_errorAnalysis.tex' % (dataDir, outdir, 'data', histo))
     os.system("sed -i -e 's|!AOREF!|%s|g' %s/%s_%s_errorAnalysis.tex" % (histo, outdir, 'data', histo))
     os.system("sed -i -e 's|!SAMPLE!|%s|g' %s/%s_%s_errorAnalysis.tex" % ('data', outdir, 'data', histo))
     os.system("sed -i -e 's|!ERRORBREAKDOWN!|%s|g' %s/%s_%s_errorAnalysis.tex" % (plotDictionary['error-analysis']['data'][histo], outdir, 'data', histo))
     os.system("sed -i -e 's|!COVMATRIX!|%s|g' %s/%s_%s_errorAnalysis.tex" % (plotDictionary['correlation-matrix']['data'][histo], outdir, 'data', histo))
     os.system("sed -i -e 's|!COVDETAILS!|%s|g' %s/%s_%s_errorAnalysis.tex" % (plotDictionary['covDetails']['data'][histo], outdir, 'data', histo))
     frames.append("%s/%s_%s_errorAnalysis.tex" % (outdir, 'data', histo))
     for sample in samples:
       sampleNameClean = sample.replace("_", " ").replace("-", " ")
       os.system('cp %s/dataMCComparison.tex %s/%s_%s_dataMCComparison.tex' % (dataDir, outdir, sample, histo))
       os.system("sed -i -e 's|!AOREF!|%s|g' %s/%s_%s_dataMCComparison.tex" % (histo, outdir, sample, histo))
       os.system("sed -i -e 's|!SAMPLE!|%s|g' %s/%s_%s_dataMCComparison.tex" % (sampleNameClean, outdir, sample, histo))
       os.system("sed -i -e 's|!DATAVSMC!|%s|g' %s/%s_%s_dataMCComparison.tex" % (plotDictionary['data-vs-mc'][sample][histo], outdir, sample, histo))
       os.system("sed -i -e 's|!CHI2CONTRIBS!|%s|g' %s/%s_%s_dataMCComparison.tex" % (plotDictionary['chi2-contribs'][sample][histo], outdir, sample, histo))
       frames.append("%s/%s_%s_dataMCComparison.tex" % (outdir, sample, histo))
     # summary
     os.system('cp %s/sectionSummary.tex %s/%s_sectionSummary.tex' % (dataDir, outdir, histo))
     os.system("sed -i -e 's|!AOREF!|%s|g' %s/%s_sectionSummary.tex" % (histo, outdir, histo))
     os.system("sed -i -e 's|!SUMMARYPLOT!|%s|g' %s/%s_sectionSummary.tex" % (plotDictionary['summary-plot'][histo], outdir, histo))
     os.system("sed -i -e 's|!SUMMARYTABLE!|%s|g' %s/%s_sectionSummary.tex" % (plotDictionary['summary-table'][histo], outdir, histo))
     frames.append("%s/%s_sectionSummary.tex" % (outdir, histo))
  os.system('cp %s/section.tex %s/%s_section.tex' % (dataDir, outdir, 'overall'))
  os.system("sed -i -e 's|!AOREF!|%s|g' %s/%s_section.tex" % ('overall summary', outdir, 'overall'))
  frames.append('%s/%s_section.tex' % (outdir, 'overall'))
  os.system('cp %s/overallSummary.tex %s/%s_overallSummary.tex' % (dataDir, outdir, 'overall'))
  os.system("sed -i -e 's|!AOREF!|%s|g' %s/%s_overallSummary.tex" % ('overall summary', outdir, 'overall'))
  os.system("sed -i -e 's|!SUMMARYTABLE!|%s|g' %s/%s_overallSummary.tex" % (plotDictionary['summary-table']['all'], outdir, 'overall'))
  frames.append("%s/%s_overallSummary.tex" % (outdir, 'overall'))
  frames.append("%s/end.tex" % dataDir)
  catLine = " ".join(frames)
  os.system(" cat %s > %s/slides_%s.tex" % (catLine, outdir, opts.data.replace('.yoda', '')))
  return " %s/slides_%s.tex" % (outdir, opts.data.replace('.yoda', ''))
 
 

◆ makeSummaryTable()

covarianceTool.makeSummaryTable	(	plotDictionary,
		outfile,
		hdataName = None )

Definition at line 143 of file covarianceTool.py.

def makeSummaryTable(plotDictionary, outfile, hdataName=None):
   # make a summary latex table of the chi2 values for each AO
   outf = open(outfile, 'w')
   samples = plotDictionary['chi2-value'].keys()
   if (hdataName):
     histos = [hdataName]
   else:
     histos = sorted(plotDictionary['chi2-value'][samples[0]].keys())
 
   line = "\\begin{tabular}{|l|"
   for hist in histos:
    line += "c|"
   line += "}"
   outf.write(line + '\n')
   outf.write("\\hline " + '\n')
   line = " $\\chi^2$ / ndof "
   for hist in histos:
     if 'superAO' in hist: hist = 'global agreement'
     line += " & %s " % hist
   line += "\\\\"
   outf.write(line + '\n')
   outf.write("\\hline" + '\n')
   for sample in samples:
     line = " %s " % sample.replace("-", " ").replace("_", " ")
     for hist in histos:
       line += " & %s" % plotDictionary['chi2-value'][sample][hist]  
     line += "\\\\"
     outf.write(line + '\n')
   outf.write("\\hline" + '\n')
   outf.write("\\end{tabular}" + '\n')
   outf.close()
 
 

◆ markupYODAwithCorrelations()

covarianceTool.markupYODAwithCorrelations	(	ref,
		corrDir,
		ignoreCorrelations )

Definition at line 419 of file covarianceTool.py.

def markupYODAwithCorrelations(ref, corrDir, ignoreCorrelations):
  # produce a new YODA file where the AOs have been marked up with
  # covariance info from an uncertainty breakdown provided as
  # either YAML or YODA files
  isYoda = False
  if ignoreCorrelations:
     return markupYODAwithCorrelationsFromYODAs(ref, 'self')
  else:
    if (corrDir is None): return ref
    for f in os.listdir(corrDir):
      if '.yoda' in f: isYoda = True
  if isYoda:
     return markupYODAwithCorrelationsFromYODAs(ref, corrDir)
  else:
     return markupYODAwithCorrelationsFromYAML(ref, corrDir)
 
 

◆ markupYODAwithCorrelationsFromYAML()

covarianceTool.markupYODAwithCorrelationsFromYAML	(		reference,
			correlationsDir )

Definition at line 292 of file covarianceTool.py.

def markupYODAwithCorrelationsFromYAML(reference, correlationsDir):
  # produce a new YODA file where the AOs have been marked up with
  # covariance info from an uncertainty breakdown provided as
  # one or more YAML files from HEPData
  reference_out = reference.replace(".yoda", "_corrs.yoda")
  reference_outf = open(reference_out, 'w')
  infiles = {}
  for f in os.listdir(correlationsDir):
    if 'submission' in f: continue
    if 'comb' in f: continue
    if "syst" in f:
      infiles.setdefault(correlationsDir + "/" + f.replace('syst', 'xsect'), [])
    else:  
      infiles.setdefault(correlationsDir + "/" + f, [])
 
  for f in infiles.keys():
    stream = open(f, "r")
    doc = yaml.load(stream)
    binLabels = {}
    binValues = {}
    binErrors = {}
    for itemtype, itemarray in doc.items():
      if itemtype == "independent_variables":  # bin labels
        for line in itemarray:
          binLabels['name'] = itemarray[0]['header']['name']
          counter = 0
          for entry in itemarray[0]['values']:
            if ('low' in entry.keys() and 'high' in entry.keys()):
              binLabels[counter] = "%f - %f" % (entry['low'], entry['high'])
            else:
              binLabels[counter] = "%f" % (entry['value'])
            counter += 1
 
      if itemtype == "dependent_variables":  # values
        for line in itemarray:
          binValues['name'] = "%s [%s]" % (line['header']['name'], '')
          binErrors['name'] = "uncertainties"
        counter = 0
        for entry in line['values']:
           binValues[counter] = entry['value']
           binErrors[counter] = {}
           if 'errors' in entry.keys():
             for error in entry['errors']:
               binErrors[counter][error['label']] = {}
               if 'symerror' in error.keys():
                 if not isFloat(error['symerror']) and '%' in (error['symerror']):
                   binErrors[counter][error['label']]['up'] = binValues[counter] * 0.01 * float(error['symerror'].replace('%', ''))
                   binErrors[counter][error['label']]['dn'] = binValues[counter] * -0.01 * float(error['symerror'].replace('%', ''))
                 else:
                   binErrors[counter][error['label']]['up'] = float(error['symerror'])
                   binErrors[counter][error['label']]['dn'] = -1 * float(error['symerror'])
               elif 'asymerror' in error.keys():
                 if not isFloat(error['asymerror']['plus']) and '%' in error['asymerror']['plus']:
                   binErrors[counter][error['label']]['up'] = binValues[counter] * 0.01 * float(error['asymerror']['plus'].replace('%', ''))
                   binErrors[counter][error['label']]['dn'] = binValues[counter] * 0.01 * float(error['asymerror']['minus'].replace('%', ''))
                 else:
                   binErrors[counter][error['label']]['up'] = float(error['asymerror']['plus'])
                   binErrors[counter][error['label']]['dn'] = 1 * float(error['asymerror']['minus'])
               else:
                 print('[ERROR] errors are neither symmetric or asymmetric... exit!')
                 exit(1)
           counter += 1
 
    if 'xsect' in f:
      systNames = binErrors[0].keys()
      binErrorsGranular = {}
      systFile = f.replace("xsect", "syst")
      streamsyst = open(systFile, "r")
      docsyst = yaml.load(streamsyst)
      binErrorsGranular['name'] = "uncertainties"
      for itemtype, itemarray in docsyst.items():
        if "variables" not in itemtype: continue
        if (itemtype == "independent_variables"):
          systNames = [v['value'] for v in itemarray[0]['values']]
        else:
          binCounter = -1
          for line in itemarray:
            binCounter += 1
            binErrorsGranular[binCounter] = {}
            if 'values' not in line.keys(): continue
            systCounter = -1
            for error in line['values']:
              systCounter += 1
              binErrorsGranular[binCounter][systNames[systCounter]] = {}
              binErrorsGranular[binCounter][systNames[systCounter]]['up'] = float(error['value']) * binValues[binCounter] * 0.01
              binErrorsGranular[binCounter][systNames[systCounter]]['dn'] = -1 * float(error['value']) * binValues[binCounter] * 0.01
      for i in range(len(binErrorsGranular.keys()) - 1):
        binErrorsGranular[i]['stat'] = {}
        binErrorsGranular[i]['stat']['up'] = binErrors[i]['stat']['up']
        binErrorsGranular[i]['stat']['dn'] = binErrors[i]['stat']['dn']
      binErrors = binErrorsGranular
    nBins = len(binLabels) - 1
    infiles[f] = [binValues, binErrors]
 
  hists = yoda.read(reference, unpatterns=".*RAW.*")
  for name in hists:
    nearestMatch = ""
    smallestDiff = 999.
    nearestMatchErrors = ""
    nominal = [(hists[name].points()[i].y) for i in range(hists[name].numPoints())]
    for f, corrs in infiles.items():
      binValues = corrs[0]
      binErrors = corrs[1]
      nBins = len(binValues) - 1
      if (len(nominal) != nBins): continue
      totalDiff = 0
      for i in range(nBins):
        totalDiff += abs(binValues[i] - nominal[i])
      if (totalDiff < smallestDiff):
        nearestMatch = f
        smallestDiff = totalDiff
        nearestMatchErrors = binErrors
    if (opts.verbosity > 1): print("[DEBUG] candidate", nominal, " nearest match is ", nearestMatch)
    if smallestDiff < 1.0:
      if (opts.verbosity > 1): print("[INFO] mapping", name, " ---> ", nearestMatch, (smallestDiff))
      corrs = yaml.dump(nearestMatchErrors, default_flow_style=True, default_style='', width=1e6)
      hists[name].setAnnotation("ErrorBreakdown", corrs)
      yoda.writeYODA(hists[name], reference_outf)
    else:
      print("[WARNING] Warning, no match found for", name)
  print("[INFO] %s has been marked up with correlation info from %s in this file:" % (reference, correlationsDir))
  print("[INFO] ---> %s " % (reference_out))
  print("[INFO] next time, you can use this file as an input instead of ", reference)
  reference_outf.close()
  return reference_out
 
 

◆ markupYODAwithCorrelationsFromYODAs()

covarianceTool.markupYODAwithCorrelationsFromYODAs	(		reference,
			correlationsDir )

Definition at line 228 of file covarianceTool.py.

def markupYODAwithCorrelationsFromYODAs(reference, correlationsDir):
  # produce a new YODA file where the AOs have been marked up with
  # covariance info from an uncertainty breakdown provided as
  # a separate YODA file for each systematic variation
  reference_out = reference.replace(".yoda", "_corrs.yoda")
  reference_outf = open(reference_out, 'w')
  infiles = {}
  if correlationsDir == 'self':
    infiles.setdefault(reference, [])
  else:
    for f in os.listdir(correlationsDir):
      if "all" in f: continue
      infiles.setdefault(correlationsDir + "/" + f, [])
  nBins = -1
  binLabels = {}
  binValues = {}
  binErrors = {}
 
  histos = [name for name in yoda.read(reference, unpatterns=".*RAW.*")]
  for histname in histos:
    hAll = yoda.read(reference, unpatterns=".*RAW.*")[histname]
    if type(hAll) is yoda.core.Histo1D: hAll = hAll.mkScatter()
    for f in infiles.keys():
      systname = f.split("/")[-1].replace(".yoda", "")
      h = yoda.read(f, unpatterns=".*RAW.*")[histname]
      if type(h) is yoda.core.Scatter2D:
        nBins = h.numPoints()
        for ipt in range(nBins):
           binValues[ipt] = h.points()[ipt].y()
           binLabels[ipt] = "%f - %f" % (h.points()[ipt].xMin(), h.points()[ipt].xMax())
           errs = h.points()[ipt].yErrs()
           errAv = (abs(errs[1]) + abs(errs[0])) * 0.5
           binErrors.setdefault(ipt, {}).setdefault(systname, {})['up'] = errs[1]
           binErrors.setdefault(ipt, {}).setdefault(systname, {})['dn'] = -1 * errs[0]
      elif type(h) is yoda.core.Histo1D:
        nBins = h.numBins
        for ipt in range(nBins):
           binValues[ipt] = h.bins()[ipt].sumW()
           binLabels[ipt] = "%f - %f" % (h.bins()[ipt].xMin(), h.bins()[ipt].xMax())
           errAv = h.bins()[ipt].sumW2()
           binErrors.setdefault(ipt, {}).setdefault(systname, {})['up'] = errAv
           binErrors.setdefault(ipt, {}).setdefault(systname, {})['dn'] = -1 * errAv
      else: continue
    if (reference != ""):
      smallestDiff = 999.
      for f in reference.split(","):
        hists = yoda.read(f, unpatterns=".*RAW.*")
        for name in hists:
            if not type(hists[name]) is yoda.core.Scatter2D: continue
            nominal = [(hists[name].points()[i].y()) for i in range(hists[name].numPoints())]
            if (len(nominal) != nBins): continue
            totalDiff = 0
            for i in range(nBins):
              totalDiff += abs(binValues[i] - nominal[i])
            if (totalDiff < smallestDiff):
              smallestDiff = totalDiff
 
    corrs = yaml.dump(binErrors, default_flow_style=True, default_style='', width=1e6)
    hAll.setAnnotation("ErrorBreakdown", corrs)
    yoda.writeYODA(hAll, reference_outf)
  reference_outf.close()
  return reference_out
 
 

◆ matchAOs()

covarianceTool.matchAOs	(	aoList1,
		aoList2,
		strategy = 0,
		recursive = True )

Definition at line 176 of file covarianceTool.py.

def matchAOs(aoList1, aoList2, strategy=0, recursive=True):
  # try to establish a 1-1 mapping between AOs in two YODA files
  # eg in case of different naming conventions
  aoMap = {}
  for ao1 in aoList1:
   aoMap.setdefault(ao1, [])
   for ao2 in aoList2:
     nBinsMatch = False
     namesMatch = False
     namesPartialMatch = False
     binWidthsMatch = False
     if (ao2.name() in ao1.name()): namesMatch = True
     if (ao1.name() in ao2.name()): namesMatch = True
     ao1NameTokens = ao1.name().split("/")[-1].split("-")
     ao2NameTokens = ao2.name().split("/")[-1].split("-")
     if len(list(set(ao1NameTokens).intersection(ao2NameTokens))) >= 2: namesPartialMatch = True
     if (ao1.numPoints() == ao2.numPoints()):
       nBinsMatch = True
       for ipt in range(ao1.numPoints()):
         ao1BinLabels = "%.2f - %.2f" % (ao1.points()[ipt].xMin(), ao1.points()[ipt].xMax())
         ao2BinLabels = "%.2f - %.2f" % (ao2.points()[ipt].xMin(), ao2.points()[ipt].xMax())
         if (ao1BinLabels == ao2BinLabels): binWidthsMatch = True
     if strategy == 0:
       if (namesMatch and nBinsMatch and binWidthsMatch):
         aoMap.setdefault(ao1, []).append(ao2)
     elif strategy == 1:
       if (nBinsMatch and binWidthsMatch):
         aoMap.setdefault(ao1, []).append(ao2)
     elif strategy == 2:
       if (nBinsMatch and binWidthsMatch and namesPartialMatch):
         aoMap.setdefault(ao1, []).append(ao2)
 
  if (len(aoMap.keys()) == len(aoList1)) and ([len(aoMap[ao]) for ao in aoList1]) == ([1 for ao in aoList1]):
    if (opts.verbosity > 0): print("[INFO] found 1-1 mapping between aoLists using strategy %d:" % strategy)
    for ao1 in aoList1:
        aoMap[ao1] = aoMap[ao1][0]
        if (opts.verbosity > 1): print("%s --> %s" % (ao1.name(), aoMap[ao1].name()))
  elif (recursive and strategy < 3):
    if (opts.verbosity > 0):
      print("[WARNING] Could not establish 1-1 mapping between aoLists using strategy", strategy, ", try strategy ", strategy + 1)
    for ao1 in aoList1:
        if (opts.verbosity > 1): print("%s --> %s" % (ao1.name(), aoMap[ao1]))
    strategy += 1
    aoMap = matchAOs(aoList1, aoList2, strategy)
  else:
    print("[ERROR] could not match AOS in the Data and MC files. Please make sure the AOs have the same binning/ number of bins (and name if possible!)")
    print("[ERROR] aos from list 1", aoList1)
    print("[ERROR] aos from list 2", aoList2)
    exit(1)
  return aoMap
 
 

◆ renameAOs()

covarianceTool.renameAOs	(		fin_name,
			aoMap )

Definition at line 74 of file covarianceTool.py.

def renameAOs(fin_name, aoMap):
  # make a new YODA file where the AOs are renamed as per the reference file
  fout_name = fin_name.replace(".yoda", ".renamed.yoda")
  fout = open(fout_name, 'w')
  aos = yoda.read(fin_name, unpatterns=".*RAW.*")
  for aopath, ao in aos.items():
    foundMatch = False
    for ao1, ao2 in aoMap.items():
      if ao1.name() == ao.name():
        ao.setAnnotation("Path", ao2.path().replace("REF/", ""))
        foundMatch = True
      if ao2.name() == ao.name():
        ao.setAnnotation("Path", ao1.path().replace("REF/", ""))
        foundMatch = True
    if foundMatch: yoda.writeYODA(ao, fout)
  fout.close()
  return fout_name
 
 

◆ returnFileContents()

covarianceTool.returnFileContents	(		filename,
			inputType = 'data' )

Definition at line 436 of file covarianceTool.py.

def returnFileContents(filename, inputType='data'):
    result = []
    aos = yoda.read(filename, unpatterns=".*RAW.*")
    for aopath, ao in aos.items():
      aoType = type(ao)
      if aoType in [yoda.Histo1D, yoda.Histo2D]: ao = ao.mkScatter()
      if aoType in [yoda.Counter]: continue
      if 'all' not in opts.filter and inputType == 'data':
        for f in opts.filter:
          if f in aopath:
            result.append(ao)
      else:
        result.append(ao)
 
    covInfoMissing = []
    for ao in result:
      corr = ao.annotation("ErrorBreakdown")
      if (corr is None):
        covInfoMissing.append(ao)
    if (len(covInfoMissing) and inputType == 'data' and not opts.ignore_corrs and not opts.corr_data):
      print("[WARNING] These DATA analysis objects are missing the correlation/covariance information:", covInfoMissing)
      print("  --> To add correlation information, you can use the eg options: --get_corr_from_web, --corr_data ")
      print("  --> You can also proceed by adding the --ignore_corrs_data in which case the chi2 is calculated")
      print("  --> from the total uncertainties and assumed to be bin-bin uncorrelated.")
      inspireID = [t for t in opts.data.split("_") if "I" in t]
      inspireID = inspireID[0].replace("I", "")
      if os.path().isdir("corrInfo/ins%s" % inspireID):
        print("[INFO] Attempting to use the information in corrInfo/ins%s to build correlation info " % inspireID)
        opts.corr_data = "corrInfo/ins%s" % inspireID
      else:
        getCorrelationInfoFromWeb()
 
    if (len(covInfoMissing) and inputType == 'mc' and not opts.ignore_corrs and not opts.corr_mc):
      print("[WARNING] These MC analysis objects are missing the correlation/covariance information:", covInfoMissing)
      print("  --> To add correlation information, you can use the eg options: --corr_mc ")
      print("  --> You can also proceed by adding the --ignore_corrs_mc in which case the chi2 is calculated")
      print("  --> from the total uncertainties and assumed to be bin-bin uncorrelated.")
      if os.path.isdir("corrInfoMC/%s" % filename.replace(".yoda", "")):
        print("[INFO] Attempting to use the information in corrInfoMC/%s to build correlation info " % filename.replace(".yoda", ""))
        opts.corr_mc = "corrInfoMC/%s" % filename.replace(".yoda", "")
    return result
 
 

Variable Documentation

◆ action

covarianceTool.action

Definition at line 34 of file covarianceTool.py.

◆ aoMap

covarianceTool.aoMap = matchAOs(histograms['data'], histograms['models'][mcnew])

Definition at line 564 of file covarianceTool.py.

◆ args

covarianceTool.args

Definition at line 49 of file covarianceTool.py.

◆ beamerPath

covarianceTool.beamerPath = makeBeamer(plotDictionary, outdir)

Definition at line 730 of file covarianceTool.py.

◆ chi2

covarianceTool.chi2

Definition at line 678 of file covarianceTool.py.

◆ chi2contribs

covarianceTool.chi2contribs

Definition at line 678 of file covarianceTool.py.

◆ chi2ContribsByRow

covarianceTool.chi2ContribsByRow = ct.chi2ContribsByRow(chi2contribs)

Definition at line 683 of file covarianceTool.py.

◆ chi2ContribsByRowYAML

covarianceTool.chi2ContribsByRowYAML = yaml.dump(chi2ContribsByRow, default_flow_style=True, default_style='', width=1e6)

Definition at line 684 of file covarianceTool.py.

◆ covData

covarianceTool.covData = ct.makeCovarianceMatrixFromToys(hdata, opts.ntoys, opts.ignore_corrs_data)

Definition at line 621 of file covarianceTool.py.

◆ covDetailsData

str covarianceTool.covDetailsData = "Size of uncertainties across range, and Data Correlation Matrix"

Definition at line 618 of file covarianceTool.py.

◆ covDetailsMC

str covarianceTool.covDetailsMC = "MC Covariance Matrix"

Definition at line 638 of file covarianceTool.py.

◆ covMC

covarianceTool.covMC = ct.makeCovarianceMatrixFromToys(hmc, opts.ntoys, opts.ignore_corrs_mc)

Definition at line 641 of file covarianceTool.py.

◆ covTotal

covarianceTool.covTotal = covData + covMC

Definition at line 654 of file covarianceTool.py.

◆ data

covarianceTool.data

Definition at line 529 of file covarianceTool.py.

◆ dataSuperAO

covarianceTool.dataSuperAO = ct.makeSuperAO(histograms['data'])

Definition at line 585 of file covarianceTool.py.

◆ default

covarianceTool.default

Definition at line 32 of file covarianceTool.py.

◆ dest

covarianceTool.dest

Definition at line 32 of file covarianceTool.py.

◆ False

covarianceTool.False

Definition at line 34 of file covarianceTool.py.

◆ filter

covarianceTool.filter

Definition at line 514 of file covarianceTool.py.

◆ headers

covarianceTool.headers = plotparser.getHeaders(hdata.path())

Definition at line 537 of file covarianceTool.py.

◆ help

covarianceTool.help

Definition at line 32 of file covarianceTool.py.

◆ histograms

dict covarianceTool.histograms

Initial value:

=  {
    'data': None,
    'covariance': {},
    'models': {},
}

Definition at line 53 of file covarianceTool.py.

◆ hmc

covarianceTool.hmc = aoMap[hdata]

Definition at line 610 of file covarianceTool.py.

◆ ignore_corrs_data

covarianceTool.ignore_corrs_data

Definition at line 516 of file covarianceTool.py.

◆ ignore_corrs_mc

covarianceTool.ignore_corrs_mc

Definition at line 517 of file covarianceTool.py.

◆ mc

covarianceTool.mc = mc.split(":")[0]

Definition at line 554 of file covarianceTool.py.

◆ mcName

dict covarianceTool.mcName = mc.split(":")[1]

Definition at line 553 of file covarianceTool.py.

◆ mcNames

dict covarianceTool.mcNames = {}

Definition at line 519 of file covarianceTool.py.

◆ mcnew

covarianceTool.mcnew = markupYODAwithCorrelations(mc, opts.corr_mc, opts.ignore_corrs_mc)

Definition at line 558 of file covarianceTool.py.

◆ mcR

dict covarianceTool.mcR = mcResults[model]

Definition at line 709 of file covarianceTool.py.

◆ mcResults

dict covarianceTool.mcResults = {}

Definition at line 602 of file covarianceTool.py.

◆ mcSuperAO

covarianceTool.mcSuperAO = ct.makeSuperAO(histograms['models'][mc])

Definition at line 589 of file covarianceTool.py.

◆ ndf

covarianceTool.ndf

Definition at line 678 of file covarianceTool.py.

◆ newmc

covarianceTool.newmc = renameAOs(mcnew, aoMap)

Definition at line 565 of file covarianceTool.py.

◆ ntoys

covarianceTool.ntoys

Definition at line 512 of file covarianceTool.py.

◆ opts

covarianceTool.opts

Definition at line 49 of file covarianceTool.py.

◆ outdir

str covarianceTool.outdir = "outputs/%s/data/plots" % (opts.data.replace(".yoda", ""))

Definition at line 532 of file covarianceTool.py.

◆ outdirplots

str covarianceTool.outdirplots = outdir + "/data-vs-%s/plots/" % mcName

Definition at line 697 of file covarianceTool.py.

◆ parser

covarianceTool.parser = optparse.OptionParser(usage="%prog [options]")

Definition at line 31 of file covarianceTool.py.

◆ passFilter

covarianceTool.passFilter = False

Definition at line 604 of file covarianceTool.py.

◆ pathName

covarianceTool.pathName = hdata.path().replace("/REF", "")

Definition at line 704 of file covarianceTool.py.

◆ plotDictionary

dict covarianceTool.plotDictionary = {'error-analysis': {}, 'covariance-matrix': {}, 'correlation-matrix': {}, 'data-vs-mc': {}, 'chi2-contribs': {}, 'chi2-value': {}, 'covDetails': {}, 'summary-plot': {}, 'summary-table': {}}

Definition at line 520 of file covarianceTool.py.

◆ plotparser

covarianceTool.plotparser = rivet.mkStdPlotParser([], [])

Definition at line 536 of file covarianceTool.py.

◆ plots

covarianceTool.plots = st.makeSystematicsPlotsWithROOT(res, outdirplots, nominalName='Data', ratioZoom=None, regexFilter=".*%s.*" % hmc.name(), regexVeto=None)

Definition at line 698 of file covarianceTool.py.

◆ prob

covarianceTool.prob

Definition at line 678 of file covarianceTool.py.

◆ res

dict covarianceTool.res = {'%s' % opts.data: '[Data]', '%s' % model: '[%s (# chi^2=%.2f/%d)]' % (mcName, chi2, ndf)}

Definition at line 696 of file covarianceTool.py.

◆ Title

covarianceTool.Title

Definition at line 540 of file covarianceTool.py.

◆ title

covarianceTool.title

Definition at line 542 of file covarianceTool.py.

◆ verbosity

covarianceTool.verbosity

Definition at line 513 of file covarianceTool.py.

◆ XLabel

covarianceTool.XLabel

Definition at line 538 of file covarianceTool.py.

◆ xLabel

covarianceTool.xLabel

Definition at line 542 of file covarianceTool.py.

Functions

Variables

Detailed Description

Function Documentation

◆ convertHistToScatter()

◆ getCorrelationInfoFromWeb()

◆ isFloat()

◆ makeBeamer()

◆ makeSummaryTable()

◆ markupYODAwithCorrelations()

◆ markupYODAwithCorrelationsFromYAML()

◆ markupYODAwithCorrelationsFromYODAs()

◆ matchAOs()

◆ renameAOs()

◆ returnFileContents()

Variable Documentation

◆ action

◆ aoMap

◆ args

◆ beamerPath

◆ chi2

◆ chi2contribs

◆ chi2ContribsByRow

◆ chi2ContribsByRowYAML

◆ covData

◆ covDetailsData

◆ covDetailsMC

◆ covMC

◆ covTotal

◆ data

◆ dataSuperAO

◆ default

◆ dest

◆ False

◆ filter

◆ headers

◆ help

◆ histograms

◆ hmc

◆ ignore_corrs_data

◆ ignore_corrs_mc

◆ mc

◆ mcName

◆ mcNames

◆ mcnew

◆ mcR

◆ mcResults

◆ mcSuperAO

◆ ndf

◆ newmc

◆ ntoys

◆ opts

◆ outdir

◆ outdirplots

◆ parser

◆ passFilter

◆ pathName

◆ plotDictionary

◆ plotparser

◆ plots

◆ prob

◆ res

◆ Title

◆ title

◆ verbosity

◆ XLabel

◆ xLabel