covarianceToolsLibrary Namespace Reference

Functions
	setCorrInfo (h, binErrors)
	addSystematicToCorrInfo (h, systName, binErrorsToAdd)
	getCorrInfo (h, names=[])
	hasAsymmetricErrors (ao)
	makeCovarianceMatrix (ao, ignore_corrs=False)
	hasValidErrorBreakdown (ao)
	makeCovarianceMatrixFromToys (ao, ntoys=10000, ignore_corrs=False)
	plotVariations (ao, outdir, label, threshold=0.05, xLabel=None, title=None)
	getDiagonalCovMatricesFromScatter (scat)
	getDiagonalCovMatricesFromHisto (scat)
	scatterToMatrix (h, bw=False)
	histoToMatrix (h, bw=False)
	printMatrix (matrix, outfile=None)
	drawMatrix (matrix, outfile, xLabel=None)
	manualChi2 (data, mc, cov, verbosity=0)
	calculateChi2 (data, mc, cov=None, verbosity=0)
	correlationMatrix (covMatrix)
	updateAOinYODA (ao, infile)
	chi2ContributionMatrix (diff, prec)
	chi2ContribsByRow (chi2contribs)
	makeSuperAO (aoList, verbosity=0)

Variables
	gErrorIgnoreLevel

Detailed Description

The `covarianceToolsLibrary.py` can be used directly to manipulate covariance info and evaluate goodness of fit, inmported into your own python script.
Examples of how to do this are provided in the `examples/covarianceTool-examples/` directory

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

Function Documentation

addSystematicToCorrInfo()

covarianceToolsLibrary.addSystematicToCorrInfo	(		h,
			systName,
			binErrorsToAdd )

Definition at line 33 of file covarianceToolsLibrary.py.

def addSystematicToCorrInfo(h, systName, binErrorsToAdd):
  corr = h.annotation('ErrorBreakdown')
  binErrors = yaml.load(corr)
  if binErrorsToAdd.keys() != binErrors.keys():
    print("[ERROR], could not add ", systName, " to ", h, " because the bins are inconsistent")
    print(binErrors.keys(), " vs ", binErrorsToAdd.keys())
    return
  for k, v in binErrorsToAdd.items():
    binErrors[k][systName] = v
  setCorrInfo(h, binErrors)
 
 

calculateChi2()

covarianceToolsLibrary.calculateChi2	(		data,
			mc,
			cov = None,
			verbosity = 0 )

Definition at line 415 of file covarianceToolsLibrary.py.

def calculateChi2(data, mc, cov=None, verbosity=0):
    ndf = data.numPoints()
    values = {}
    for name, obj in {'data': data, 'mc': mc}.items():
      if type(obj) in (yoda.Scatter1D, yoda.Scatter2D, yoda.Scatter3D):
         values[name] = scatterToMatrix(obj)
      else:  # Histo*D hopefully !
        values[name] = histoToMatrix(obj, bw=True)  
 
    v = values['data'].copy()
    v -= values['mc'].copy()
    if cov is None:
     covData = makeCovarianceMatrix(data)
     covMC = makeCovarianceMatrix(mc)
     cov = covData + covMC
 
    if (cov.shape[0] != cov.shape[1]):
      print("[ERROR] the specified covariance matrix is not square!", cov)
      exit(1)
    if (cov.shape[0] != v.shape[0]):
      print("[ERROR] the specified covariance matrix is incompatible with the vector of data-MC", cov, v)
      exit(1)
 
    precision_matrix = LA.inv(cov.copy())
 
    if (verbosity > 1):
      print("[INFO]: precision matrix:", precision_matrix)
 
    vT = v.copy().transpose()
    chi2tmp = (v.dot(precision_matrix)).dot(vT)
    chi2 = chi2tmp[0][0]
    pvalue = (1 - stats.chi2.cdf(chi2, ndf))
 
    return chi2, ndf, pvalue, chi2ContributionMatrix(v, precision_matrix)
 
 

chi2ContribsByRow()

covarianceToolsLibrary.chi2ContribsByRow

(

chi2contribs

)

Definition at line 479 of file covarianceToolsLibrary.py.

def chi2ContribsByRow(chi2contribs):
    nbins = chi2contribs.shape[0]
    mo = {}
    for j in range(nbins):
       mo[j] = 0.0
       for i in range(nbins):
         mo[j] += float(chi2contribs[i][j])
    return mo
 
 

chi2ContributionMatrix()

covarianceToolsLibrary.chi2ContributionMatrix	(		diff,
			prec )

Definition at line 469 of file covarianceToolsLibrary.py.

def chi2ContributionMatrix(diff, prec):
    nbins = prec.shape[0]
    diffT = diff.copy().transpose()
    mo = np.zeros([nbins, nbins])
    for j in range(nbins):
       for i in range(nbins):
         mo[i][j] = diff[0][j] * prec[i][j] * diffT[i][0]
    return mo
 
 

correlationMatrix()

covarianceToolsLibrary.correlationMatrix

(

covMatrix

)

Definition at line 451 of file covarianceToolsLibrary.py.

def correlationMatrix(covMatrix):
  nbins = covMatrix.shape[0]
  corr = np.zeros([nbins, nbins])
  for i in range(nbins):
     sigma_i = covMatrix[i][i]
     for j in range(nbins):
        sigma_j = covMatrix[j][j]
        corr[i][j] = covMatrix[i][j] / np.sqrt(sigma_i * sigma_j)
 
  return corr
 
 

drawMatrix()

covarianceToolsLibrary.drawMatrix	(		matrix,
			outfile,
			xLabel = None )

Definition at line 336 of file covarianceToolsLibrary.py.

def drawMatrix(matrix, outfile, xLabel=None):
 
   nCols = matrix.shape[0]
   nRows = matrix.shape[1]
   assert(nCols == nRows)
   nBins = nCols
   r.gROOT.SetBatch()
   lat = r.TLatex()
   lat.SetTextSize(0.02)
   lat.SetTextAngle(45)
   lat.SetTextAlign(22)
   Red = array('d', [1.0, 1.0, 0.0])
   Grn = array('d', [0.0, 1.0, 0.0])
   Blu = array('d', [0.0, 1.0, 1.0])
   Length = array('d', [0.0, 0.5, 1.0])
   r.TColor.CreateGradientColorTable(len(Length), Length, Red, Grn, Blu, 50)
   if ('chi2' in outfile): nRows += 1
   th2 = r.TH2D(outfile.split("/")[-1], outfile.split("/")[-1], nRows, 0, nRows, nBins, 0, nBins)
   th2.SetTitle("")
   totalOverall = 0.
   totalsPerRow = []
   for j in range(nBins):
      th2.GetYaxis().SetBinLabel(j + 1, "%d" % (nBins - j))
      total = 0.
      for i in range(nBins):
        th2.GetXaxis().SetBinLabel(i + 1, "%d" % (i + 1))
        th2.Fill(i, nBins - j - 1, matrix[i][j])
        total += matrix[i][j]
      totalOverall += total
      if('chi2' in outfile): th2.Fill(nBins, nBins - j - 1, total)
      if('chi2' in outfile): th2.GetXaxis().SetBinLabel(nBins + 1, "TOTAL =%.2f" % totalOverall)
      totalsPerRow.append(total)
   tc = r.TCanvas("c", "c", 500, 500)
   tc.SetRightMargin(0.2)
   if('covariance' in outfile): tc.SetLogz()
   th2.Draw("colz")
   for j in range(nBins):
      for i in range(nBins):
        lat.DrawLatex(i + 0.5, nBins - j - 0.5, "%.2f" % matrix[i][j])
      if('chi2' in outfile): lat.DrawLatex(nBins + 0.5, nBins - j - 0.5, "%.2f" % totalsPerRow[j])
   th2.SetMaximum(max(abs(th2.GetMaximum()), abs(th2.GetMinimum())))
   th2.SetMinimum(-1 * (th2.GetMaximum()))
   th2.SetStats(0)
   lat = r.TLatex()
   lat.SetNDC()
   lat.SetTextSize(0.05)
   lat.DrawLatex(0.1, 0.91, "#it{ATLAS} #bf{Internal}")
   lat.SetTextSize(0.035)
   if xLabel:
      lat.SetTextAlign(33)
      lat.SetTextSize(0.04)
      lat.DrawLatex(0.9, 0.05, "#bf{%s}" % xLabel.replace("$", "").replace("\\text", ""))
      lat.SetTextAngle(90)
      lat.DrawLatex(0.02, 0.9, "#bf{%s}" % xLabel.replace("$", "").replace("\\text", ""))
   tc.SaveAs(outfile + ".pdf")
   tc.SaveAs(outfile + ".png")
 
 

getCorrInfo()

covarianceToolsLibrary.getCorrInfo	(		h,
			names = [] )

Definition at line 45 of file covarianceToolsLibrary.py.

def getCorrInfo(h, names=[]):
  if not (h.hasAnnotation('ErrorBreakdown')):
    print("[ERROR] analsyis object", h, " has no stored correlation info!")
    return None
  binErrors = yaml.load(h.annotation('ErrorBreakdown'))
  if len(names) > 0:
    binErrorsFiltered = {}
    for k, v in binErrors.items():  # loop through bins
      foundName = False
      for name in names:
        for k2, v2 in v.items():  # loop through systematics
          if k2 == name:
            foundName = True
            binErrorsFiltered.setdefault(k, {})[k2] = v2
      if not foundName:
        print("[WARNING] cannot find systematic with name ", name)
    binErrors = binErrorsFiltered
  return binErrors
 
 

getDiagonalCovMatricesFromHisto()

covarianceToolsLibrary.getDiagonalCovMatricesFromHisto

(

scat

)

Definition at line 279 of file covarianceToolsLibrary.py.

def getDiagonalCovMatricesFromHisto(scat):
 npts = scat.numBins
 cov_up = np.zeros([npts, npts])
 cov_dn = np.zeros([npts, npts])
 for i in range(npts):
   err = scat.bins[i].xStdErr()
   cov_up[i][i] = err * err
   cov_dn[i][i] = err * err
 return[cov_dn, cov_up]
 
 

getDiagonalCovMatricesFromScatter()

covarianceToolsLibrary.getDiagonalCovMatricesFromScatter

(

scat

)

Definition at line 268 of file covarianceToolsLibrary.py.

def getDiagonalCovMatricesFromScatter(scat):
 npts = scat.numPoints()
 cov_up = np.zeros([npts, npts])
 cov_dn = np.zeros([npts, npts])
 for i in range(npts):
   yed_, yeu_ = scat.points()[i].yErrs()
   cov_up[i][i] = yeu_ * yeu_
   cov_dn[i][i] = yed_ * yed_
 return[cov_dn, cov_up]
 
 

hasAsymmetricErrors()

covarianceToolsLibrary.hasAsymmetricErrors

(

ao

)

Definition at line 65 of file covarianceToolsLibrary.py.

def hasAsymmetricErrors(ao):
   nbins = ao.numPoints()
   corr = yaml.load(ao.annotation('ErrorBreakdown'))
   if len(corr) == 0: return True
   systList = corr[0].keys()
   hasAsymmErrs = False
   for sname in systList:
     for ibin in range(nbins):
       up = ((corr[ibin][sname]['up']))
       dn = ((corr[ibin][sname]['dn']))
       if (up != -1 * dn):
         hasAsymmErrs = True
         break
   return hasAsymmErrs
 
 

hasValidErrorBreakdown()

covarianceToolsLibrary.hasValidErrorBreakdown

(

ao

)

Definition at line 108 of file covarianceToolsLibrary.py.

def hasValidErrorBreakdown(ao):
  if not ao.hasAnnotation("ErrorBreakdown"): return False
  if len(ao.annotation("ErrorBreakdown")) == 0: return False
  corrs = getCorrInfo(ao)
  for iBin, binErrs in corrs.items():
    binTotal = [0., 0.]
    for syst, err in binErrs.items():
      binTotal[0] = (binTotal[0] ** 2 + err['up'] ** 2) ** 0.5
      binTotal[1] = (binTotal[1] ** 2 + err['dn'] ** 2) ** 0.5
    if binTotal[0] == 0 and binTotal[1] == 0: return False
  return True
 
 

histoToMatrix()

covarianceToolsLibrary.histoToMatrix	(		h,
			bw = False )

Definition at line 304 of file covarianceToolsLibrary.py.

def histoToMatrix(h, bw=False):
  npts = h.numBins
  mo = np.zeros([npts, npts])
  for i in range(npts):
    for j in range(npts):
      mo[0][i] = h.bins[i].sumW()
      if bw:
        width = h.bins[i].xWidth()
        mo[0][i] *= 1. / width
  return mo
 
 

makeCovarianceMatrix()

covarianceToolsLibrary.makeCovarianceMatrix	(		ao,
			ignore_corrs = False )

Definition at line 81 of file covarianceToolsLibrary.py.

def makeCovarianceMatrix(ao, ignore_corrs=False):
   nbins = ao.numPoints()
   corr = ao.annotation('ErrorBreakdown') if '1.7' in yoda.version() else yaml.load(ao.annotation('ErrorBreakdown'))
   dummyM = np.outer(range(nbins), range(nbins))
   covM = np.zeros(dummyM.shape)
   if len(corr) == 0:
     for ibin in range(nbins):
       covM[ibin][ibin] = ((ao.points()[ibin].yErrs()[0] + ao.points()[ibin].yErrs()[1]) / 2) ** 2
       if covM[ibin][ibin] == 0: covM[ibin][ibin] = 1
     print("[WARNING], ao ", ao.path, " has no errors. Setting cov martix to unit... but consider excluding it !")
     return covM
   systList = corr[0].keys()
   for sname in systList:
     systErrs = np.zeros(nbins)
     for ibin in range(nbins):
       systErrs[ibin] = ((abs(corr[ibin][sname]['up'])) + (abs(corr[ibin][sname]['dn']))) * 0.5  # up/dn are considered the same since NoToys assumes symmetric errors
     if (ignore_corrs or 'stat' in sname):
        covM += np.diag(systErrs * systErrs)
     else:
        covM += np.outer(systErrs, systErrs)
   if LA.det(covM) == 0:
     printMatrix(covM)
     print("[ERROR], the above cov matrix is singular for ", ao.path(), " ! exit")
     print("(if this is a cov matrix for a super-AO, perhaps you have several highly correlated distributions in your set. Try vetoing some.)")
   return covM
 
 

makeCovarianceMatrixFromToys()

covarianceToolsLibrary.makeCovarianceMatrixFromToys	(		ao,
			ntoys = 10000,
			ignore_corrs = False )

Definition at line 121 of file covarianceToolsLibrary.py.

def makeCovarianceMatrixFromToys(ao, ntoys=10000, ignore_corrs=False):
   nbins = ao.numPoints()
   corr = yaml.load(ao.annotation('ErrorBreakdown'))
   systList = corr[0].keys()
   toys = np.zeros([ntoys, nbins])
 
   for itoy in range(ntoys):
      shift = np.zeros(len(systList))
      delta = np.zeros(nbins)
      isyst = 0
      for sname in systList:
         if (ignore_corrs or 'stat' in sname):
            for n in range(nbins):
               lrand = np.random.normal(0, 1)  # bin-bin independent stat fluctuations
               s = ((corr[n][sname]['up'])) if lrand >= 0. else ((corr[n][sname]['dn']))
               delta[n] += abs(lrand) * s
         else:
            lrand = np.random.normal(0, 1)
            shift[isyst] = lrand  # bin-bin correlated fluctuations
 
            for n in range(nbins):
               s = ((corr[n][sname]['up'])) if lrand >= 0. else ((corr[n][sname]['dn']))
               delta[n] += abs(lrand) * s  # linear interpolation
         isyst += 1
 
      for n in range(nbins):
         toys[itoy][n] = (1. + delta[n])
 
   cov = np.cov(toys, rowvar=0)
   if LA.det(cov) == 0:
     printMatrix(cov)
     print("[ERROR], the above cov matrix is singular for ", ao.path, " ! exit")
     print("(if this is a cov matrix for a super-AO, perhaps you have several highly correlated distributions in your set. Try vetoing some.)")
   return cov
 
 

makeSuperAO()

covarianceToolsLibrary.makeSuperAO	(		aoList,
			verbosity = 0 )

Definition at line 489 of file covarianceToolsLibrary.py.

def makeSuperAO(aoList, verbosity=0):
    aoOut = yoda.core.Scatter2D()
    pointCounter = 0
    corrOut = {}
    pointsOut = []
    allSystematics = []
    for aoIn in aoList:
      corrIn = aoIn.annotation('ErrorBreakdown') if '1.7' in yoda.version() else yaml.load(aoIn.annotation('ErrorBreakdown'))
      if len(corrIn) == 0: continue
      for ipt in range(aoIn.numPoints()):
        for syst in corrIn[ipt].keys():
          if syst not in allSystematics: allSystematics.append(syst)
 
    for aoIn in sorted(aoList, key=lambda x: x.name):
      corrIn = aoIn.annotation('ErrorBreakdown') if '1.7' in yoda.version() else yaml.load(aoIn.annotation('ErrorBreakdown'))
      if len(corrIn) == 0: continue
      for ipt in range(aoIn.numPoints()):
        aoOut.addPoint(yoda.core.Point2D(pointCounter, aoIn.points()[ipt].y()))
        totalErrorUp = 0
        for syst in allSystematics:
          if syst in corrIn[ipt].keys():
             corrOut.setdefault(pointCounter, {})[syst] = corrIn[ipt][syst]
             totalErrorUp += corrIn[ipt][syst]['up'] * corrIn[ipt][syst]['up']
          else:
             corrOut.setdefault(pointCounter, {})[syst] = {'up': 0.0, 'dn': 0.0}
        pointsOut.append(aoIn.points()[ipt])
        pointCounter += 1
    for i in range(aoOut.numPoints()):
      if (verbosity) > 1: print("[INFO] Super AO points ", aoOut.points()[i])
    corrs = yaml.dump(corrOut, default_flow_style=True, default_style='', width=1e6)
    aoOut.setAnnotation("ErrorBreakdown", corrs)
    name = aoList[0].path.replace(aoList[0].name, "")
    name += "superAO"
    for ao in sorted(aoList, key=lambda x: x.name):
      name += "_%s" % ao.name
    aoOut.setAnnotation("Path", name)
    return aoOut

manualChi2()

covarianceToolsLibrary.manualChi2	(		data,
			mc,
			cov,
			verbosity = 0 )

Definition at line 394 of file covarianceToolsLibrary.py.

def manualChi2(data, mc, cov, verbosity=0):
   if (verbosity > 2):
     print()
     print()
     print("[INFO] Manual Chi2 Calculation, (verbosity)= ", (verbosity > 0))
   Nbins = cov.shape[0]
   chi2 = 0
   for i in range(Nbins):
    nEvData = data[0][i]
    nEvMC = mc[0][i]
    sigmaSq = cov[i][i]
    sigma = (sigmaSq) ** 0.5
    residual = nEvData - nEvMC
    if (verbosity > 2):
      print("[INFO] Manual Chi2 Bin i=%d: data=%f, mc=%f, res=%f vs sigma=%f res/sigma=%.2f, (r/s) ** 2=%.2f" % (i, nEvData, nEvMC, residual, sigma, float(residual) / sigma, (float(residual) / sigma) ** 2))
    chi2 += (residual / sigma) ** 2
   if (verbosity > 2):
     print("[INFO] FINAL MANUAL CHI2= ", chi2)
   return chi2
 
 

plotVariations()

covarianceToolsLibrary.plotVariations	(		ao,
			outdir,
			label,
			threshold = 0.05,
			xLabel = None,
			title = None )

Definition at line 157 of file covarianceToolsLibrary.py.

def plotVariations(ao, outdir, label, threshold=0.05, xLabel=None, title=None):
   if ao.dim() != 2: return
   tgraphs = {}
   tgraphs['all'] = [r.TGraphAsymmErrors(), r.TGraphAsymmErrors(), r.TGraphAsymmErrors(), r.TGraphAsymmErrors()]
   maxErr = 0
 
   nbins = ao.numPoints()
   corr = ao.annotation('ErrorBreakdown') if '1.7' in yoda.version() else yaml.load(ao.annotation('ErrorBreakdown'))
 
   if len(corr) == 0: return
   systList = corr[0].keys()
 
   all_up = [0 for i in range(nbins)]
   all_dn = [0 for i in range(nbins)]
   for sname in systList:
     tgu = r.TGraphAsymmErrors()
     tgu_line = r.TGraphAsymmErrors()
     tgd = r.TGraphAsymmErrors()
     tgd_line = r.TGraphAsymmErrors()
     pt = 0
     draw = 0
     for i in range(nbins):
        nominalx = ao.points()[i].x()
        nominaly = ao.points()[i].y()
        xerr = abs(nominalx - ao.points()[i].xMin())
        tgu.SetPoint(pt, nominalx, 1)
        tgd.SetPoint(pt, nominalx, 1)
        if nominaly == 0: continue
        eup = 1 - (((corr[i][sname]['up'])) + nominaly) / nominaly
        edn = 1 - (((corr[i][sname]['dn'])) + nominaly) / nominaly
        all_up[i] = (all_up[i] ** 2 + eup ** 2) ** 0.5
        all_dn[i] = (all_dn[i] ** 2 + edn ** 2) ** 0.5
        if (abs(eup) > threshold): draw = 1
        if (abs(edn) > threshold): draw = 1
        if (abs(edn) > threshold): draw = 1
        if abs(all_up[i]) > maxErr: maxErr = abs(all_up[i])
        tgu.SetPointError(pt, xerr, xerr, 0, eup)
        tgd.SetPointError(pt, xerr, xerr, 0, edn)
        tgu_line.SetPoint(pt, nominalx, 1 + eup)
        tgd_line.SetPoint(pt, nominalx, 1 + edn)
        tgu_line.SetPointError(pt, xerr, xerr, 0, 0)
        tgd_line.SetPointError(pt, xerr, xerr, 0, 0)
        tgraphs['all'][0].SetPoint(pt, nominalx, nominaly / nominaly)
        tgraphs['all'][1].SetPoint(pt, nominalx, nominaly / nominaly)
        tgraphs['all'][2].SetPoint(pt, nominalx, 1 - all_dn[i])
        tgraphs['all'][3].SetPoint(pt, nominalx, 1 + all_up[i])
        tgraphs['all'][0].SetPointError(pt, xerr, xerr, 0, -1 * all_dn[i])
        tgraphs['all'][1].SetPointError(pt, xerr, xerr, 0, all_up[i])
        tgraphs['all'][2].SetPointError(pt, xerr, xerr, 0, 0)
        tgraphs['all'][3].SetPointError(pt, xerr, xerr, 0, 0)
        pt += 1
     if (draw): tgraphs[sname] = [tgd, tgu, tgd_line, tgu_line]
   r.gROOT.SetBatch()
   tc = r.TCanvas("c", "c", 500, 500)
   same = 0
   itg = 0
   leg = r.TLegend(.13, .6, .9, .8)
   leg.SetNColumns(2)
   leg.SetBorderSize(0)
   leg.SetFillColor(0)
   leg.SetFillStyle(0)
   leg.SetTextFont(42)
   leg.SetTextSize(0.025)
 
   for tgname, tg in sorted(tgraphs.items(), key=lambda x: abs(x[1][1].GetErrorYhigh(0)), reverse=True):
      itg += 1
      isCorr = not (('stat' in tgname) or ('all' in tgname))
      if 'nominal' == tgname: continue
      if 'mc' == tgname: continue
      tg[0].SetFillColorAlpha(0 + itg, 0.3)
      tg[1].SetFillColorAlpha(0 + itg, 0.3)
      tg[0].SetMarkerColor(1)  # kBlack
      tg[2].SetMarkerStyle(23)
      tg[2].SetMarkerColor(0 + itg)
      tg[2].SetLineColor(0 + itg)
      tg[3].SetMarkerStyle(22)
      tg[3].SetMarkerColor(0 + itg)
      tg[3].SetLineColor(0 + itg)
      leg.AddEntry(tg[0], tgname, "f")
      if same:
        tg[0].Draw("p2 same")
        tg[1].Draw("p2 same")
        if isCorr: tg[2].Draw("P same")
        if isCorr: tg[3].Draw("P same ")
      else:
        tg[0].SetMaximum((1. + maxErr) * 1.33)
        tg[0].SetMinimum(1. - maxErr)
        tg[0].Draw("AP2")
        tg[1].Draw("P2 same ")
        if isCorr: tg[2].Draw("P same")
        if isCorr: tg[3].Draw("P same")
        same = 1
   leg.Draw()
   lat = r.TLatex()
   leg.SetTextFont(42)
   lat.SetNDC()
   lat.SetTextSize(0.05)
   lat.DrawLatex(0.1, 0.91, "#it{ATLAS} #bf{Internal}")
   lat.SetTextSize(0.035)
   lat.DrawLatex(0.13, 0.83, "#bf{All variations >%1.1f%% anywhere in range:}" % (threshold * 100))
   if xLabel:
      lat.SetTextAlign(33)
      lat.SetTextSize(0.04)
      lat.DrawLatex(0.9, 0.05, "#bf{%s}" % xLabel.replace("$", "").replace("\\text", ""))
   lat.SetTextAngle(90)
   lat.DrawLatex(0.02, 0.9, "#bf{Relative Uncertainty}")
   outfile = "%s/%s-variations" % (outdir, label)
   tc.SaveAs(outfile + ".pdf")
   tc.SaveAs(outfile + ".png")
 
 

printMatrix()

covarianceToolsLibrary.printMatrix	(		matrix,
			outfile = None )

Definition at line 316 of file covarianceToolsLibrary.py.

def printMatrix(matrix, outfile=None):
   if isinstance(outfile, str()): outfile = open("%s.txt" % outfile, 'w')
   nCols = matrix.shape[0]
   nRows = matrix.shape[1]
   colNames = "X "
   for i in range(nCols):
     colNames += "%d " % i
   if outfile is not None: outfile.write(colNames + "\n")
   else: print(colNames + "\n")
 
   for j in range(nRows):
      rowString = "%d| " % j
      for i in range(nCols):
        rowString += "%.6f " % matrix[i][j]
      if outfile is not None: outfile.write(rowString + "\n")
      else: print(rowString + "\n")
   if outfile is not None: outfile.write("Eigenvalues %s\n" % (" ".join(["%.3f" % val for val in LA.eigvals((matrix))])))
   else: print("Eigenvalues %s\n" % (" ".join(["%.6f" % val for val in LA.eigvals((matrix))])))
 
 

scatterToMatrix()

covarianceToolsLibrary.scatterToMatrix	(		h,
			bw = False )

Definition at line 290 of file covarianceToolsLibrary.py.

def scatterToMatrix(h, bw=False):
  npts = h.numPoints()
  dim = h.dim()
  mo = np.zeros([npts, npts])
  for i in range(npts):
    for j in range(npts):
      mo[0][i] = h.points()[i].val(dim)
      if bw:
        width = (abs(h.points()[i].errs(dim)[0]))
        width += (abs(h.points()[i].errs(dim)[1]))
        mo[0][i] *= 1. / width
  return mo
 
 

setCorrInfo()

covarianceToolsLibrary.setCorrInfo	(		h,
			binErrors )

`h` AO (the Scatter you want to set the correlation info for)
`binErrors`

Definition at line 24 of file covarianceToolsLibrary.py.

def setCorrInfo(h, binErrors):
  """
  `h` AO (the Scatter you want to set the correlation info for)
  `binErrors`
  """
  corrIn = yaml.dump(binErrors, default_flow_style=True, default_style='', width=1e6)
  h.setAnnotation("ErrorBreakdown", corrIn)
 
 

updateAOinYODA()

covarianceToolsLibrary.updateAOinYODA	(		ao,
			infile )

Definition at line 463 of file covarianceToolsLibrary.py.

def updateAOinYODA(ao, infile):
    aos = yoda.read(infile)
    aos[ao.path] = ao
    yoda.write(sorted(aos.values()), infile)
 
 

Variable Documentation

gErrorIgnoreLevel

covarianceToolsLibrary.gErrorIgnoreLevel

Definition at line 21 of file covarianceToolsLibrary.py.