def main(reader_name,
tags,
lut_path,
ptmin,
ptmax,
ymin=None,
ymax=None,
tag_name=None,
logx=False,
logy=False,
leg_pos=[0.74, 0.2, 0.90, 0.4],
particles=None,
eta=0,
dnch_deta=100,
rmin=None,
add_eta_label=True,
add_alice3_label=True,
save=None,
background=False,
use_p_over_z=True,
aod=None,
study_label="ALICE 3 study"):
gROOT.LoadMacro(reader_name)
gROOT.LoadMacro("style.C")
reader_name = reader_name.split(".")[-2]
reader = getattr(__import__('ROOT', fromlist=[reader_name]), reader_name)
style = getattr(__import__('ROOT', fromlist=["style"]), "style")
style()
p = {
"el": "e",
"pi": "#pi",
"ka": "K",
"pr": "p",
"de": "d",
"tr": "t",
"he3": "^{3}He"
}
charge = {"el": 1, "pi": 1, "ka": 1, "pr": 1, "de": 1, "tr": 1, "he3": 2}
p_colors = {
"el": "#e41a1c",
"pi": "#377eb8",
"ka": "#4daf4a",
"pr": "#984ea3",
"de": "#ff7f00",
"tr": "#999999",
"he3": "#a65628"
}
if particles is not None:
to_remove = []
for i in p:
if i not in particles:
to_remove.append(i)
for i in to_remove:
p.pop(i)
latex = TLatex()
latex.SetTextAlign(33)
canvas = reader_name
canvas = canvas.replace("lutRead_", "")
canvas = TCanvas(canvas, canvas, 800, 800)
canvas.Divide(2, 2)
drawn = [canvas]
drawn_graphs = {}
drawn_frames = {}
if ymin is None:
if "_dca" in reader_name:
ymin = 0.1
elif "_pt" in reader_name:
ymin = 1.
elif "_eff" in reader_name:
ymin = 0.
if ymax is None:
if "_dca" in reader_name:
ymax = 1e4
elif "_pt" in reader_name:
ymax = 100.
elif "_eff" in reader_name:
ymax = 115.
def adjust_pad():
if logx:
gPad.SetLogx()
if logy:
gPad.SetLogy()
counter = 1
leg = None
if tag_name is not None:
leg = TLegend(*leg_pos)
if add_eta_label:
label = f"#eta = {int(eta)}"
label += " dN_{Ch}/d#eta ="
label += f" {int(dnch_deta)}"
if rmin is not None:
label += " R_{min} = " + rmin
else:
leg.SetHeader()
leg.SetHeader(label)
leg.SetLineColor(0)
drawn.append(leg)
def draw_study_label(x=0.5, y=0.9):
latex = TLatex()
latex.SetTextAlign(13)
drawn.append(latex.DrawLatexNDC(x, y, " ".join(study_label)))
for i in p:
c = f"{canvas.GetName()}_{i}"
c = TCanvas(c, c, 800, 800)
drawn.append(c)
adjust_pad()
frame = c.DrawFrame(ptmin, ymin, ptmax, ymax, "")
frame.SetDirectory(0)
drawn_frames[i] = frame
g_list = []
extra = {}
cols = [
'#e41a1c', '#377eb8', '#4daf4a', '#984ea3', '#ff7f00', '#ffff33'
]
for k, j in enumerate(tags):
lut = f"{lut_path}/lutCovm.{i}.{j}.dat"
if j == "":
lut = f"{lut_path}/lutCovm.{i}.dat"
if not path.isfile(lut):
print("LUT file", lut, "does not exist")
return
g = reader(lut, eta, dnch_deta)
if g.GetN() <= 0:
print("Skipping", g.GetName(), "because empty graph")
continue
if len(g_list) == 0:
frame.GetXaxis().SetTitle(g.GetXaxis().GetTitle())
frame.GetYaxis().SetTitle(g.GetYaxis().GetTitle())
if use_p_over_z:
for j in range(g.GetN()):
if "_pt" in reader_name:
g.SetPoint(j,
g.GetPointX(j) / charge[i],
g.GetPointY(j) / charge[i])
else:
g.SetPoint(j,
g.GetPointX(j) / charge[i], g.GetPointY(j))
frame.GetXaxis().SetTitle("#it{p}_{T}/z (GeV/#it{c})")
col = TColor.GetColor(cols[len(g_list)])
g.SetLineColor(col)
g.SetLineStyle(1)
g.SetLineWidth(3)
g.Draw("samel")
if aod is not None:
f_aod = TFile(aod, "READ")
if "_eff" in reader_name:
extra[g.GetName()] = f_aod.Get(
"qa-tracking-efficiency-kaon/pt/num")
extra[g.GetName()].Divide(
f_aod.Get("qa-tracking-efficiency-kaon/pt/num"),
f_aod.Get("qa-tracking-efficiency-kaon/pt/den"), 1, 1,
"B")
extra[g.GetName()].Scale(100)
extra[g.GetName()].Draw("SAME")
extra[g.GetName()].SetDirectory(0)
elif "_pt" in reader_name:
extra[g.GetName()] = f_aod.Get(
"qa-tracking-efficiency-kaon/pt/num")
extra[g.GetName()].Divide(
f_aod.Get("qa-tracking-efficiency-kaon/pt/num"),
f_aod.Get("qa-tracking-efficiency-kaon/pt/den"), 1, 1,
"B")
extra[g.GetName()].Scale(100)
extra[g.GetName()].Draw("SAME")
extra[g.GetName()].SetDirectory(0)
f_aod.Close()
print("Drawing", g.GetName())
if tag_name is not None and counter == 1:
leg.AddEntry(g, tag_name[k], "l")
g_list.append(g)
drawn_graphs[i] = g_list
if len(g_list) <= 0:
print("Nothing drawn!")
continue
drawn.append(latex.DrawLatexNDC(0.9, 0.9, p[i]))
if leg is not None:
leg.Draw()
draw_study_label(.4, .91)
gPad.Update()
canvas.cd(counter)
clone = c.DrawClonePad()
if counter != 1:
l = gPad.GetListOfPrimitives()
for i in l:
cn = i.ClassName()
if cn == "TLegend":
l.Remove(i)
elif cn == "TLatex":
if "ALICE" in i.GetTitle():
l.Remove(i)
drawn.append(clone)
c.SaveAs(f"/tmp/{c.GetName()}.png")
gPad.Update()
counter += 1
canvas_all_species = None
if len(tags) == 1:
canvas_all_species = TCanvas("all_spec_" + canvas.GetName(),
"all_spec_" + canvas.GetName(), 800, 800)
drawn.append(canvas_all_species)
canvas_all_species.cd()
drawn_graphs_all_spec = {}
leg_all_spec = TLegend(*leg_pos)
leg_all_spec.SetNColumns(2)
leg_all_spec.SetLineColor(0)
drawn.append(leg_all_spec)
for i in drawn_graphs:
if canvas_all_species.GetListOfPrimitives().GetEntries() == 0:
drawn_frames[i].Draw()
g_list = []
for j in drawn_graphs[i]:
g_list.append(j.Clone())
g_list[-1].SetName(g_list[-1].GetName() + "_color")
g_list[-1].SetLineColor(TColor.GetColor(p_colors[i]))
g_list[-1].Draw("same")
leg_all_spec.AddEntry(g_list[-1], p[i], "L")
drawn_graphs_all_spec[i] = g_list
for i in drawn_graphs_all_spec:
drawn_graphs[i + "_allspec"] = drawn_graphs_all_spec[i]
leg_all_spec.Draw()
if add_alice3_label:
draw_study_label()
latex = TLatex()
latex.SetTextAlign(13)
latex.SetTextSize(0.04)
if tag_name is not None:
drawn.append(latex.DrawLatexNDC(0.5, 0.80, tag_name[0]))
drawn.append(
latex.DrawLatexNDC(
0.4, 0.82, f"#eta = {int(eta)}" + "\n dN_{Ch}/d#eta =" +
f" {int(dnch_deta)}" +
("\n R_{min} = " + rmin if rmin is not None else "")))
adjust_pad()
canvas_all_species.Update()
canvas_all_species.SaveAs(f"/tmp/{canvas_all_species.GetName()}.png")
if save is None:
canvas.SaveAs(f"/tmp/lut_{canvas.GetName()}.root")
else:
fo = TFile(save, "RECREATE")
fo.cd()
canvas.Write()
if canvas_all_species is not None:
canvas_all_species.Write()
for i in drawn_graphs:
for j in drawn_graphs[i]:
j.Write()
if not background:
input("Done, press enter to continue")
class plots(object):
""" Extract all plots from Middleware scan. """
def __init__(self, inputfile, outputfolder=''):
""" Initialize object variables. """
self._rootfile = TFile(inputfile)
self._directory = outputfolder
self._canvas = TCanvas()
self._histos = []
self._histos_good = []
self._histos_bad = []
self._shifts = []
self._widths = []
makedirs(outputfolder)
gStyle.SetOptStat(000000000)
def getAllPlots(self):
""" Get all the plots. """
for kname in self._getKeys():
# Clear all objects from canvas
self._canvas.Clear()
# Get histogram and draw it
histo = self._rootfile.Get(kname)
histo.Draw()
# Save it as pdf
self._save(kname)
def getScurvePerCbc(self, cbcs=range(0, 8), channels=-1):
""" Fit all Scurves and put them into one histogram. Also plot shifts
(offsets) and widths (noise).
Parameters:
cbcs: List of CBC's to plot
channels: Number of channels to plot; -1 for all
"""
for cbc in cbcs:
self._drawScurves(cbc, channels)
def _drawScurves(self, cbc, channels):
""" Make the fits to draw the Scurves.
Parameters:
cbc: CBC to plot
channels: Number of channels to plot; -1 for all
"""
# Directory name in rootfile
dirname = 'Final0'
# Counter of fitted histograms
count = 0
# Chi2 limit to distinguish good from bad fits
chi2 = .05
# Loop over all keys in subdirectory
for kname in self._getKeys(dirname):
# Only plot a limited number of channels
if channels > 0 and count >= channels:
break
# Only select S-Curves of a given CBC
if not kname.startswith('{}/Scurve_Be0_Fe0_Cbc{}'.format(
dirname, cbc)):
continue
# Error function with guessed parameters
errf = TF1('errf', '.5 + .5*erf((x-[0])/[1])', 0., 254.)
errf.SetParameter(0, 120.) # shift
errf.SetParameter(1, 10.) # width
# Fit
histo = self._rootfile.Get(kname)
histo.Fit('errf', 'RQ')
# If fit failed, skip it
try:
histo.GetFunction('errf').GetParameter(0)
except TypeError:
continue
# Colors!
histo.SetLineColor(
self._getColor(count, (channels > 0 and channels < 19)))
histo.SetMarkerColor(
self._getColor(count, (channels > 0 and channels < 19)))
histo.GetFunction('errf').SetLineColor(
self._getColor(count, (channels > 0 and channels < 19)))
# Markers!
histo.SetMarkerStyle(count % 15 + 20)
histo.SetMarkerSize(.8)
# Save values for future use
self._shifts.append(histo.GetFunction('errf').GetParameter(0))
self._widths.append(histo.GetFunction('errf').GetParameter(1))
self._histos.append(histo)
# Save histograms either in list of good fits or in list of bad fits
if histo.GetFunction('errf').GetChisquare() <= chi2:
self._histos_good.append(histo)
else:
self._histos_bad.append(histo)
# Save histogram with fit
histo.Draw()
self._save('{}_fit'.format(kname))
# Save smoothed histogram
histo.Draw('C HIST')
self._save('{}_smooth'.format(kname))
# Count the number of histograms
count += 1
# Draw all error functions in one histogram
self._draw_all_errf(self._histos, 'S-curves for CBC {}'.format(cbc),
'scurves_cbc{}'.format(cbc))
# Draw all good error functions in one histogram
self._draw_all_errf(
self._histos_good,
'S-curves for CBC {} (#chi^{{2}} < {})'.format(cbc, chi2),
'scurves_cbc{}_good'.format(cbc))
# Draw all bad error functions in one histogram
self._draw_all_errf(
self._histos_bad,
'S-curves for CBC {} (#chi^{{2}} > {})'.format(cbc, chi2),
'scurves_cbc{}_bad'.format(cbc))
# Draw all fitted error functions in one histogram
self._draw_all_errf_fit(self._histos,
'Fitted S-curves for CBC {}'.format(cbc),
'scurves_cbc{}_fit'.format(cbc))
# Draw all fitted error functions and measurements in one histogram
self._draw_all_errf_fit_meas(self._histos,
'Fitted S-curves for CBC {}'.format(cbc),
'scurves_cbc{}_fit_meas'.format(cbc))
def _draw_all_errf(self, histos, title, name):
""" Draw all error functions in one histogram
Parameters:
histos: List of histograms to plot
title: Title of histogram
name: Name of histogram
"""
self._canvas.Clear()
for idx, histo in enumerate(histos):
if idx == 0:
# Dynamically set plot range
histo.GetXaxis().SetRangeUser(self._getXmin(), self._getXmax())
histo.Draw('C HIST')
histo.SetTitle(title)
histo.GetXaxis().SetTitle('VCth units')
histo.GetYaxis().SetTitle('Occupancy')
else:
histo.Draw('C HIST SAME')
self._save(name)
self._save('{}_log'.format(name), True)
def _draw_all_errf_fit(self, histos, title, name):
""" Draw all error functions in one histogram
Parameters:
histos: List of histograms to plot
title: Title of histogram
name: Name of histogram
"""
self._canvas.Clear()
for idx, histo in enumerate(histos):
# Get fitted function
func = histo.GetFunction('errf')
if idx == 0:
# Dynamically set plot range
func.SetRange(self._getXmin(), self._getXmax())
func.Draw()
func.SetTitle(title)
func.GetXaxis().SetTitle('VCth units')
func.GetYaxis().SetTitle('Occupancy')
else:
func.Draw('SAME')
self._save(name)
self._save('{}_log'.format(name), True)
def _draw_all_errf_fit_meas(self, histos, title, name):
""" Draw all error functions in one histogram
Parameters:
histos: List of histograms to plot
title: Title of histogram
name: Name of histogram
"""
self._canvas.Clear()
for idx, histo in enumerate(histos):
if idx == 0:
# Dynamically set plot range
histo.GetXaxis().SetRangeUser(self._getXmin(), self._getXmax())
histo.Draw('P')
histo.SetTitle(title)
histo.GetXaxis().SetTitle('VCth units')
histo.GetYaxis().SetTitle('Occupancy')
else:
histo.Draw('P SAME')
self._save(name)
self._save('{}_log'.format(name), True)
def _getKeys(self, subdir=None):
""" Loop over all keys in a given subfolder.
Parameters:
subdir: Get keys only from that directory, including all
subdirectories
"""
# If subdir is defined, loop over keys in folder, otherwise loop over
# keys in rootfile
if subdir:
obj = self._rootfile.Get(subdir)
else:
obj = self._rootfile
# Loop over all keys
for key in obj.GetListOfKeys():
if subdir:
kname = '{}/{}'.format(subdir, key.GetName())
else:
kname = key.GetName()
# If key is a folder, go into that folder and loop over all keys
if key.IsFolder():
for knamesub in self._getKeys(kname):
yield knamesub
else:
yield kname
def _save(self, name, logy=False):
""" Save histogram as *.pdf.
Parameters:
name: Name of *.pdf, possibly including path; directories are
created on the fly
logy: Plot Y-axis in log
"""
# Go into directory if it is defined
if self._directory:
cwd = getcwd()
# Create directory on filesystem
if not path.exists(self._directory):
makedirs(self._directory)
chdir(self._directory)
# If canvas is to be stored in a subdirectory, create it
if '/' in name:
subdir = name[:name.find('/')]
if not path.exists(subdir):
makedirs(subdir)
# Set Y-axis to log if requested
if logy:
self._canvas.SetLogy()
# This is probably not failsafe, empirically, the first primitive
# is the frame, the second one an object, if this is not the case,
# it might fail
try:
self._canvas.GetListOfPrimitives().At(
1).GetYaxis().SetRangeUser(1e-7, 2.)
except AttributeError:
LGR.warning('Cannot set Y-Axis to log')
# Save as *.pdf
self._canvas.SaveAs('{}.pdf'.format(name))
# Unset log scale
if logy:
self._canvas.SetLogy(False)
try:
self._canvas.GetListOfPrimitives().At(
1).GetYaxis().SetRangeUser(1e-7, 1.05)
except AttributeError:
pass
# Go back to original working directories
if self._directory:
chdir(cwd)
def _getXmin(self):
""" Get minimum on X-Axis to plot.
"""
return min([
shift - 5 * width
for shift, width in zip(self._shifts, self._widths)
])
def _getXmax(self):
""" Get maximum on X-Axis to plot.
"""
return max([
shift + 5 * width
for shift, width in zip(self._shifts, self._widths)
])
def _getColor(self, idx, nice=False):
""" Get color for a given index (channel).
Parameters:
idx: Channel number
nice: if True, get a handful of easily distinguishable colors; if
False, get all colors
"""
# List with all colors
colors = []
if nice:
colors.extend(range(2, 10))
else:
#colors.extend(range(390, 405))
colors.extend(range(394, 405))
colors.extend(range(406, 421))
colors.extend(range(422, 437))
colors.extend(range(590, 605))
colors.extend(range(606, 621))
colors.extend(range(622, 637))
colors.extend(range(791, 911))
return colors[idx % len(colors)]
def mbc_single_3s(evtfile,
mc,
setMres,
setGamma,
setR,
sp1,
sp2,
sp3,
fa,
fb,
setmd,
setp,
setxi,
setN1,
setN2,
setNbkgd1,
setNbkgd2,
title1,
title2,
epsfile,
txtfile,
ymin=0.5,
cuts=None,
err_type='SYMM',
test=False):
from ROOT import (gROOT, RooRealVar, RooCategory, RooArgSet, RooDataSet,
RooFit, RooGaussian, RooArgList, RooAddPdf,
RooSimultaneous, RooArgusBG, RooFormulaVar,
RooDLineShape, RooAbsData, RooDataHist, TCanvas, kRed,
kBlue, kGreen, kMagenta, TPaveText)
set_root_style(stat=1, grid=0)
# // sp1 = sigma of signal
# // sp2 = ratio of sigmas betwwen sigma2 sigma 1
# // sp3 = ratio of sigmas betwwen sigma3 sigma 2
# // fa, fb, - fractions
# // xi_side - slope of argus
# // p_side - power of argus
# mc = 1 Monte Carlo Model: EvtGenModels/Class/EvtVPHOtoVISR.cc
# mc = 3 Data Model: with BES 2007 paper (BES2006 lineshape hepex/0612056)
mbc = RooRealVar('mbc', 'Beam constrained mass', 1.83, 1.89, 'GeV')
ebeam = RooRealVar('ebeam', 'Ebeam', 1.8815, 1.892, 'GeV')
dflav = RooCategory('dflav', 'D flavor')
dflav.defineType('dflav', 1)
dflav.defineType('dbarflav', -1)
if cuts != None:
if 'kkmass' in cuts:
kkmass = RooRealVar('kkmass', 'KK invariant mass', 0.97, 1.90,
'GeV')
ras = RooArgSet(mbc, ebeam, kkmass, dflav)
dataset = RooDataSet.read(evtfile, ras)
elif 'kpimass' in cuts:
kpimass = RooRealVar('kpimass', 'Kpi invariant mass', 0.6, 1.4,
'GeV')
ras = RooArgSet(mbc, ebeam, kpimass, dflav)
dataset = RooDataSet.read(evtfile, ras)
else:
raise NameError(cuts)
sys.stdout.write('Using cuts: %s...' % cuts)
dataset = dataset.reduce(cuts)
sys.stdout.write(' selected %s events.\n' % dataset.numEntries())
else:
ras = RooArgSet(mbc, ebeam, dflav)
dataset = RooDataSet.read(evtfile, ras)
res = RooRealVar("datares", "datares", mc)
mres = RooRealVar("mres", "mres", setMres)
gamma = RooRealVar('gamma', 'gamma', setGamma)
r = RooRealVar('r', 'r', setR)
sigmaE = RooRealVar("sigmaE", "sigmaE", 0.0021)
sigmap1 = RooRealVar("sigmap1", "sigmap1", sp1, 0.002, 0.040)
scalep2 = RooRealVar("scalep2", "scalep2", 2.00, 1.500, 5.500)
scalep3 = RooRealVar("scalep3", "scalep3", 5.00, 3.00, 10.000)
scalep2.setVal(sp2)
scalep2.setConstant(1)
scalep3.setVal(sp3)
scalep3.setConstant(1)
as12 = RooArgList(sigmap1, scalep2)
sigmap2 = RooFormulaVar("sigmap2", "sigma2", "sigmap1*scalep2", as12)
as123 = RooArgList(sigmap1, scalep2, scalep3)
sigmap3 = RooFormulaVar("sigmap3", "sigma3", "sigmap1*scalep2*scalep3",
as123)
md = RooRealVar("md", "md", setmd, 1.863, 1.875)
f2 = RooRealVar("f2", "f2", fa)
f3 = RooRealVar("f3", "f3", fb)
al23 = RooArgList(f2, f3)
f1 = RooFormulaVar("f1", "f1", "1.0-f2-f3", al23)
# Construct signal shape
fcn1_1 = RooDLineShape("DLineshape1_1", "DLineShape1_1", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap1, md, res)
fcn1_2 = RooDLineShape("DLineshape1_2", "DLineShape1_2", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap2, md, res)
fcn1_3 = RooDLineShape("DLineshape1_3", "DLineShape1_3", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap3, md, res)
fcn2_1 = RooDLineShape("DLineshape2_1", "DLineShape2_1", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap1, md, res)
fcn2_2 = RooDLineShape("DLineshape2_2", "DLineShape2_2", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap2, md, res)
fcn2_3 = RooDLineShape("DLineshape2_3", "DLineShape2_3", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap3, md, res)
alf1_123 = RooArgList(fcn1_1, fcn1_2, fcn1_3)
af12 = RooArgList(f1, f2)
signal1_3 = RooAddPdf("signal1_3", "signal1_3", alf1_123, af12)
alf2_123 = RooArgList(fcn2_1, fcn2_2, fcn2_3)
signal2_3 = RooAddPdf("signal2_3", "signal2_3", alf2_123, af12)
p = RooRealVar("p", "p", setp, 0.1, 1.5)
xi = RooRealVar("xi", "xi", setxi, -100.0, -0.1)
Bkgd1 = RooArgusBG("argus1", "argus1", mbc, ebeam, xi, p)
Bkgd2 = RooArgusBG("argus2", "argus2", mbc, ebeam, xi, p)
shapes1 = RooArgList(signal1_3)
shapes1.add(signal1_3)
shapes1.add(Bkgd1)
shapes2 = RooArgList(signal2_3)
shapes2.add(signal2_3)
shapes2.add(Bkgd2)
N1 = RooRealVar("N1", "N1", setN1, 0.0, 200000000.0)
N2 = RooRealVar("N2", "N2", setN2, 0.0, 200000000.0)
Nbkgd1 = RooRealVar("Nbkgd1", "Nbkgd1", setNbkgd1, 0.0, 200000000.0)
Nbkgd2 = RooRealVar("Nbkgd2", "Nbkgd2", setNbkgd2, 0.0, 200000000.0)
yields1 = RooArgList(N1)
yields1.add(N1)
yields1.add(Nbkgd1)
yields2 = RooArgList(N2)
yields2.add(N2)
yields2.add(Nbkgd2)
totalPdf1 = RooAddPdf("totalPdf1", "totalPdf1", shapes1, yields1)
totalPdf2 = RooAddPdf("totalPdf2", "totalPdf2", shapes2, yields2)
totalPdf = RooSimultaneous("totalPdf", "totalPdf", dflav)
totalPdf.addPdf(totalPdf1, "dflav")
totalPdf.addPdf(totalPdf2, "dbarflav")
# Check fitTo options at:
# http://root.cern.ch/root/html512/RooAbsPdf.html#RooAbsPdf:fitTo
#
# Available fit options:
# "m" = MIGRAD only, i.e. no MINOS
# "s" = estimate step size with HESSE before starting MIGRAD
# "h" = run HESSE after MIGRAD
# "e" = Perform extended MLL fit
# "0" = Run MIGRAD with strategy MINUIT 0
# (no correlation matrix calculation at end)
# Does not apply to HESSE or MINOS, if run afterwards.
# "q" = Switch off verbose mode
# "l" = Save log file with parameter values at each MINUIT step
# "v" = Show changed parameters at each MINUIT step
# "t" = Time fit
# "r" = Save fit output in RooFitResult object
# Available optimizer options
# "c" = Cache and precalculate components of PDF that exclusively
# depend on constant parameters
# "2" = Do NLL calculation in multi-processor mode on 2 processors
# "3" = Do NLL calculation in multi-processor mode on 3 processors
# "4" = Do NLL calculation in multi-processor mode on 4 processors
MINUIT = 'ermh4'
if err_type == 'ASYM':
MINUIT = 'erh4'
if test:
sys.stdout.write('Will save epsfile as: %s \n' % epsfile)
sys.stdout.write('Will save txtfile as: %s \n' % txtfile)
return
if dataset.numEntries() == 0:
N1.setVal(0)
N2.setVal(0)
else:
# Start Fitting
fitres = totalPdf.fitTo(dataset, MINUIT)
fitres.Print('v')
# Save plots
canvas = TCanvas('canvas', 'mbc', 1200, 400)
canvas.Divide(3, 1)
canvas_1 = canvas.GetListOfPrimitives().FindObject('canvas_1')
canvas_2 = canvas.GetListOfPrimitives().FindObject('canvas_2')
canvas_1.SetLogy(1)
canvas_2.SetLogy(1)
LineColorRed = RooFit.LineColor(kRed)
LineColorBlue = RooFit.LineColor(kBlue)
LineWidth = RooFit.LineWidth(1) #0.6)
# Plot the D
canvas.cd(1)
mbcFrame = mbc.frame()
mbcFrame = mbc.frame(60)
dflav.setLabel('dflav')
ebas = RooArgSet(ebeam, dflav)
ebeamdata = RooDataHist("ebeamdata", "ebeamdata", ebas, dataset)
dataset.plotOn(mbcFrame, RooFit.Cut("dflav==dflav::dflav"))
mbcFrame.getAttMarker().SetMarkerSize(0.6)
mbcFrame.Draw()
Slice = RooFit.Slice(dflav)
ProjWData = RooFit.ProjWData(ebas, ebeamdata)
totalPdf.plotOn(mbcFrame, LineColorRed, LineWidth, Slice, ProjWData)
chisq1 = mbcFrame.chiSquare() * mbcFrame.GetNbinsX()
mbcFrame.Draw()
as_bkg1 = RooArgSet(Bkgd1)
cp_bkg1 = RooFit.Components(as_bkg1)
totalPdf.plotOn(mbcFrame, cp_bkg1, Slice, LineColorBlue, LineWidth,
ProjWData)
mbcFrame.SetTitle(title1)
mbcFrame.SetMinimum(ymin)
mbcFrame.Draw()
# Plot the D bar
canvas.cd(2)
mbcFrame = mbc.frame()
mbcFrame = mbc.frame(60)
dflav.setLabel('dbarflav')
ebas = RooArgSet(ebeam, dflav)
ebeamdata = RooDataHist("ebeamdata", "ebeamdata", ebas, dataset)
dataset.plotOn(mbcFrame, RooFit.Cut("dflav==dflav::dbarflav"))
mbcFrame.getAttMarker().SetMarkerSize(0.6)
mbcFrame.Draw()
Slice = RooFit.Slice(dflav)
ProjWData = RooFit.ProjWData(ebas, ebeamdata)
totalPdf.plotOn(mbcFrame, LineColorRed, LineWidth, Slice, ProjWData)
chisq2 = mbcFrame.chiSquare() * mbcFrame.GetNbinsX()
mbcFrame.Draw()
as_bkg2 = RooArgSet(Bkgd2)
cp_bkg2 = RooFit.Components(as_bkg2)
totalPdf.plotOn(mbcFrame, cp_bkg2, Slice, LineColorBlue, LineWidth,
ProjWData)
mbcFrame.SetTitle(title2)
mbcFrame.SetMinimum(ymin)
mbcFrame.Draw()
# Plot Statistics Box
canvas.cd(3)
mbcFrame = mbc.frame()
paramWin1 = totalPdf.paramOn(mbcFrame, dataset, "", 2, "NELU", 0.1, 0.9,
0.9)
mbcFrame.GetXaxis().SetLabelSize(0)
mbcFrame.GetXaxis().SetTickLength(0)
mbcFrame.GetXaxis().SetLabelSize(0)
mbcFrame.GetXaxis().SetTitle("")
mbcFrame.GetXaxis().CenterTitle()
mbcFrame.GetYaxis().SetLabelSize(0)
mbcFrame.GetYaxis().SetTitleSize(0.03)
mbcFrame.GetYaxis().SetTickLength(0)
paramWin1.getAttText().SetTextSize(0.06)
mbcFrame.Draw()
mbcFrame.SetTitle("Fit Parameters")
ATextBox = TPaveText(.1, .1, .8, .2, "BRNDC")
tempString = "#chi^{2}_{1} = %.1f, #chi^{2}_{2} = %.1f" % (chisq1, chisq2)
ATextBox.AddText(tempString)
ATextBox.SetFillColor(0)
ATextBox.SetBorderSize(1)
mbcFrame.addObject(ATextBox)
mbcFrame.Draw()
canvas.Print(epsfile)
rootfile = epsfile.replace('.eps', '.root')
canvas.Print(rootfile)
# Save fitting parameters
pars = [N1, N2, Nbkgd1, Nbkgd2, md, p, sigmap1, xi]
save_fit_result(pars, txtfile, err_type=err_type, verbose=1)
