def resolutionGraph( tree, xVar, yVar, title, measure, cut='', weightExpression='' ):
## Create and fill a resolution graph for this dataset, i.e. resolution of y vs. x
# @ param tree TTree object used to create the histogram
# @ param xVar Variable object defining the xAxis and the draw command
# @ param yVar Variable object defining the xAxis and the draw command
# @ param title histogram title
# @param measure Measure object to evaluate the resolution
# @ param cut Cut object (optional)
# @ param weightExpression weight expression (optional)
# @ return the generated histogram
bins = xVar.binning.bins
title = '%s vs %s' % (yVar.title, xVar.title) if not title else title
name = 'h%s_%s' % ( title.replace(' ', '_').replace('(', '').replace(')',''), uuid.uuid1() )
from ROOT import TGraphAsymmErrors
graph = TGraphAsymmErrors( xVar.binning.nBins )
graph.SetNameTitle( name, '%s;%s;%s' % (title, xVar.axisLabel, measure.getDecoratedLabel( yVar ) ) )
# create a histogram of y for each bin in x and evaluate measure
for iBin in xrange( xVar.binning.nBins ):
low = bins[iBin]
up = bins[iBin+1]
xMean = low + 0.5 * (up - low)
xErr = xMean - low
myCut = (Cut( '%s >= %s && %s < %s' % (xVar.command, low, xVar.command, up) ) + cut) * weightExpression
values, weights = getValuesFromTree( tree, yVar.command, myCut.cut )
yMean, yErrLow, yErrUp = measure.calculateFromValues( values, weights )
graph.SetPoint( iBin, xMean, yMean )
graph.SetPointError( iBin, xErr, xErr, yErrLow, yErrUp )
return graph
def histToGraph(hist, name='', keepErrors=True, poissonErrors=True):
## Helper method to convert a histogram to a corresponding graph
# @hist TH1 object
# @name name of the graph (default is name of histogram)
# @keepErrors decide if the y-errors should be propagated to the graph
# @poissonErrors decide if the y-errors should be calculated as Poisson errors
# @return graph
if not name:
name = 'g%s' % (hist.GetName())
from ROOT import TGraphAsymmErrors
nBins = hist.GetNbinsX()
graph = TGraphAsymmErrors(nBins)
graph.SetNameTitle(name, hist.GetTitle())
xAxis = hist.GetXaxis()
for i in xrange(nBins):
xVal = xAxis.GetBinCenter(i + 1)
yVal = hist.GetBinContent(i + 1)
graph.SetPoint(i, xVal, yVal)
graph.SetPointEXlow(i, abs(xVal - xAxis.GetBinLowEdge(i + 1)))
graph.SetPointEXhigh(i, abs(xVal - xAxis.GetBinUpEdge(i + 1)))
if keepErrors:
if poissonErrors:
lo, hi = calculatePoissonErrors(yVal)
graph.SetPointEYlow(i, lo)
graph.SetPointEYhigh(i, hi)
else:
graph.SetPointEYlow(i, hist.GetBinErrorLow(i + 1))
graph.SetPointEYhigh(i, hist.GetBinErrorUp(i + 1))
# copy the style
graph.SetMarkerStyle(hist.GetMarkerStyle())
graph.SetMarkerColor(hist.GetMarkerColor())
graph.SetMarkerSize(hist.GetMarkerSize())
graph.SetLineStyle(hist.GetLineStyle())
graph.SetLineColor(hist.GetLineColor())
graph.SetLineWidth(hist.GetLineWidth())
graph.SetFillColor(hist.GetFillColor())
graph.SetFillStyle(hist.GetFillStyle())
return graph
def main():
"""
Main function
"""
parser = argparse.ArgumentParser(description="Arguments to pass")
parser.add_argument(
"configfile_name",
metavar="text",
default="config_Dplus_pp5TeV.yml",
help="input yaml config file",
)
parser.add_argument("--batch",
action="store_true",
default=False,
help="suppress video output")
args = parser.parse_args()
if args.batch:
gROOT.SetBatch(True)
# load info from config file
with open(args.configfile_name, "r") as yml_configfile:
cfg = yaml.safe_load(yml_configfile)
frac_method = cfg["fraction"]
histos, norm = load_inputs(cfg)
# consistency check of bins
ptlims = {}
for histo in ["rawyields", "acceffp", "acceffnp"]:
ptlims[histo] = get_hist_binlimits(histos[histo])
if (histo != "rawyields"
and not np.equal(ptlims[histo], ptlims["rawyields"]).all()):
print("\033[91mERROR: histo binning not consistent. Exit\033[0m")
sys.exit(10)
# compute cross section
axistit_cross = "d#sigma/d#it{p}_{T} (pb GeV^{-1} #it{c})"
axistit_cross_times_br = "d#sigma/d#it{p}_{T} #times BR (pb GeV^{-1} #it{c})"
axistit_pt = "#it{p}_{T} (GeV/#it{c})"
axistit_fprompt = "#if{f}_{prompt}"
gfraction = TGraphAsymmErrors(0)
gfraction.SetNameTitle("gfraction", f";{axistit_pt};{axistit_fprompt}")
hptspectrum = TH1F(
"hptspectrum",
f";{axistit_pt};{axistit_cross}",
len(ptlims["rawyields"]) - 1,
ptlims["rawyields"],
)
hptspectrum_wo_br = TH1F(
"hptspectrum_wo_br",
f";{axistit_pt};{axistit_cross_times_br}",
len(ptlims["rawyields"]) - 1,
ptlims["rawyields"],
)
for i_pt, (ptmin, ptmax) in enumerate(
zip(ptlims["rawyields"][:-1], ptlims["rawyields"][1:])):
pt_cent = (ptmax + ptmin) / 2
pt_delta = ptmax - ptmin
rawy = histos["rawyields"].GetBinContent(i_pt + 1)
rawy_unc = histos["rawyields"].GetBinError(i_pt + 1)
eff_times_acc_prompt = histos["acceffp"].GetBinContent(i_pt + 1)
eff_times_acc_nonprompt = histos["acceffnp"].GetBinContent(i_pt + 1)
ptmin_fonll = (
histos["FONLL"]["nonprompt"]["central"].GetXaxis().FindBin(ptmin *
1.0001))
ptmax_fonll = (
histos["FONLL"]["nonprompt"]["central"].GetXaxis().FindBin(ptmax *
0.9999))
crosssec_nonprompt_fonll = [
histos["FONLL"]["nonprompt"][pred].Integral(
ptmin_fonll, ptmax_fonll, "width") / (ptmax - ptmin)
for pred in histos["FONLL"]["nonprompt"]
]
# compute prompt fraction
if frac_method == "Nb":
frac = compute_fraction_nb( # BR already included in FONLL prediction
rawy,
eff_times_acc_prompt,
eff_times_acc_nonprompt,
crosssec_nonprompt_fonll,
pt_delta,
1.0,
1.0,
norm["events"],
norm["sigmaMB"],
)
elif frac_method == "fc":
crosssec_prompt_fonll = [
histos["FONLL"]["prompt"][pred].Integral(
ptmin_fonll, ptmax_fonll, "width") / (ptmax - ptmin)
for pred in histos["FONLL"]["prompt"]
]
frac = compute_fraction_fc(
eff_times_acc_prompt,
eff_times_acc_nonprompt,
crosssec_prompt_fonll,
crosssec_nonprompt_fonll,
)
# compute cross section times BR
crosssec, crosssec_unc = compute_crosssection(
rawy,
rawy_unc,
frac[0],
eff_times_acc_prompt,
ptmax - ptmin,
1.0,
norm["sigmaMB"],
norm["events"],
1.0,
frac_method,
)
hptspectrum.SetBinContent(i_pt + 1, crosssec / norm["BR"])
hptspectrum.SetBinError(i_pt + 1, crosssec_unc / norm["BR"])
hptspectrum_wo_br.SetBinContent(i_pt + 1, crosssec)
hptspectrum_wo_br.SetBinError(i_pt + 1, crosssec_unc)
gfraction.SetPoint(i_pt, pt_cent, frac[0])
gfraction.SetPointError(i_pt, pt_delta / 2, pt_delta / 2,
frac[0] - frac[1], frac[2] - frac[0])
# create plots
style_hist = StyleObject1D()
style_hist.markercolor = kAzure + 4
style_hist.markerstyle = kFullCircle
style_hist.markersize = 1
style_hist.linecolor = kAzure + 4
style_hist.linewidth = 2
style_hist.draw_options = "P"
fig_crosssec = ROOTFigure(1,
1,
column_margin=(0.14, 0.035),
row_margin=(0.1, 0.035),
size=(600, 800))
fig_crosssec.axes(label_size=0.025, title_size=0.030)
fig_crosssec.axes("x", title=axistit_pt, title_offset=1.5)
fig_crosssec.axes("y", title=axistit_cross_times_br, title_offset=1.8)
fig_crosssec.define_plot(0, 0, y_log=True)
fig_crosssec.add_object(hptspectrum_wo_br, style=style_hist)
fig_crosssec.create()
output_dir = cfg["output"]["directory"]
if not os.path.exists(output_dir):
os.makedirs(output_dir)
fig_crosssec.save(
os.path.join(output_dir, f'{cfg["output"]["filename"]}.pdf'))
# save output file
output_file = TFile(
os.path.join(output_dir, f'{cfg["output"]["filename"]}.root'),
"recreate")
hptspectrum.Write()
hptspectrum_wo_br.Write()
gfraction.Write()
for hist in histos:
if isinstance(histos[hist], TH1):
histos[hist].Write()
else:
for flav in histos[hist]:
for pred in histos[hist][flav]:
histos[hist][flav][pred].Write()
output_file.Close()
