def plot_data_vs_refold(args, regularisation_settings, tau):
'''
Plot the differences between the unfolded and refolded distributions
TODO Include also with best tau - redo unfolding with best tau then come here
'''
from ROOT import gStyle
variable = regularisation_settings.variable
channel = regularisation_settings.channel
plot_outpath = regularisation_settings.outpath.replace('tables/', 'plots/')+'tauscan/taus/'
make_folder_if_not_exists(plot_outpath)
# tau as string name for output
tau = str(tau).replace('.', 'p')
outfile = plot_outpath+'data_vs_refold_'+channel+'_'+variable+'_tau_'+tau+'.pdf'
if args.run_measured_as_data:
outfile = plot_outpath+'measured_vs_refold_'+channel+'_'+variable+'_tau_'+tau+'.pdf'
if args.run_smeared_measured_as_data:
outfile = plot_outpath+'smeared_vs_refold_'+channel+'_'+variable+'_tau_'+tau+'.pdf'
if args.unfolded_binning:
outfile = outfile.replace('.pdf', '_unf_binning.pdf')
c = TCanvas('c1','c1',1000,800)
gStyle.SetOptStat(0)
p1 = TPad("pad1", "p1",0.0,0.2,1.0,1.0,21)
p1.SetFillColor(0);
p1.Draw()
p2 = TPad("pad2", "p2",0.0,0.0,1.0,0.2,22)
p2.SetFillColor(0);
p2.Draw()
p1.cd()
regularisation_settings.h_data.SetTitle("Data vs Refolded Data;;NEvents")
regularisation_settings.h_data.Draw()
regularisation_settings.h_refolded.SetLineColor(2)
regularisation_settings.h_refolded.Draw("same")
leg1 = TLegend(0.7, 0.8, 0.9, 0.9)
leg1.SetLineColor(0)
leg1.SetFillColor(0)
leg1.AddEntry(regularisation_settings.h_data, "Data")
leg1.AddEntry(regularisation_settings.h_refolded, "Refolded Data")
leg1.Draw()
p2.cd()
h_ratio = regularisation_settings.h_data.Clone()
h_ratio.Divide(regularisation_settings.h_refolded)
h_ratio.SetTitle(";"+variable+";")
h_ratio.SetLineColor(1);
h_ratio.Draw()
c.SaveAs(outfile)
c.Delete()
print "Written plots to {outfile}".format(outfile = outfile)
return
def _calibrate_spectrum(self, h, title, proc, func='pol1', plot=True):
"""
Calibrates a DeltaE/E versus x spectrum based on the profile.
"""
import os
from UserCode.HGCalMaskResolutionAna.RootTools import buildMedianProfile
prof = buildMedianProfile(h)
prof.Fit(func)
calibGr = prof.GetListOfFunctions().At(0).Clone(h.GetName() + title +
'_calib')
if plot:
from ROOT import TCanvas, TLatex
c = TCanvas('c', 'c', 500, 500)
c.SetTopMargin(0.05)
c.SetBottomMargin(0.1)
c.SetLeftMargin(0.12)
c.SetRightMargin(0.1)
h.Draw('colz')
prof.Draw('e2p')
tex = TLatex()
tex.SetTextFont(42)
tex.SetTextSize(0.04)
tex.SetNDC()
tex.DrawLatex(0.12, 0.96, '#bf{CMS} #it{simulation preliminary}')
tex.DrawLatex(0.15, 0.88, title)
tex.SetTextAlign(31)
tex.DrawLatex(0.97, 0.96, proc)
c.SaveAs(
os.path.join(self.outpath,
h.GetName() + '_' + title + '.png'))
prof.Delete()
c.Delete()
return calibGr
class Plotter(object):
populations = { "World" : 7.774797e9,
"China" : 1.439323776e9,
"US" : 331.002651e6,
"Ukraine" : 43.733762e6,
"Italy" : 60.461826e6,
"France" : 65.273511e6,
"India" : 1.380004385e9,
"Spain" : 46.750411e6 }
day = 3600. * 24.
s = 1.
fill_redish = 46
fill_sandy = 43
fill_greenish = 30
# names = ("confirmed", "deaths", "recovered")
def __init__( self, country,
draw_seq = ("confirmed", "recovered", "deaths"),
derivative = 0,
to_fit = True,
fit_start = "03/22/20",
fit_end = "03/28/20",
fit_hist = "confirmed",
draw_fit = True,
floating_average = -1 ):
super(Plotter, self).__init__()
self.country = country
self.data = {
"confirmed" : self.get_data("confirmed"),
"deaths" : self.get_data("deaths"),
"recovered" : self.get_data("recovered")
}
self.sequence = draw_seq
self.derivative = derivative
self.to_fit = to_fit
self.fit_start = fit_start
self.fit_end = fit_end
self.fit_hist = fit_hist
self.draw_fit = draw_fit
self.has_fit = False
self.make_def()
if to_fit:
self.fit()
if floating_average != -1:
self.do_floating_average(floating_average)
def fit(self):
self.func = fit_hist(self.hists[self.fit_hist],
self.fit_start,
self.fit_end)
self.has_fit = True
def make_hist(self, x, y, name, color):
gr = TH1D(name, name, len(x), x[0], x[-1])
for x_i, y_i, i in zip(x, y, range(1, len(x)+1)):
if y_i == 0.:
gr.SetBinContent(i, 0. + 1e-20)
# gr.SetBinError(i, 0.)
else:
gr.SetBinContent(i, y_i + 1e-20)
gr.SetBinError(i, y_i*0.05)
# gr.SetBinError(i, sqrt(y_i))
# gr.Sumw2()
gr.GetXaxis().SetTimeDisplay(1)
gr.SetLineColor(color)
gr.SetMarkerStyle(21)
gr.SetMarkerSize(0.5)
gr.SetFillStyle(3002)
gr.SetFillColor(color)
gr.GetXaxis().SetNdivisions(16)
gr.GetXaxis().SetLabelSize(0.02)
return gr
def hist_from_data(self, data_type, color):
x, y = self.data[data_type]
for i in range(self.derivative):
y = diferentiate(y)
return self.make_hist(x, y, data_type, color)
def zoom_axis(self, begin, end):
begin = date_to_ut(begin)
end = date_to_ut(end)
for key in self.hists:
self.hists[key].GetXaxis().SetRangeUser(begin, end)
# self.draw()
def draw(self, log = False):
self.setup_graphics()
if log:
self.c.SetLogy()
for i in range(0, len(self.sequence)):
if i == 0:
self.hists[self.sequence[0]].Draw("MIN0 HIST E1")
else:
self.hists[self.sequence[i]].Draw("HIST E1 SAME")
self.legend.Draw()
if self.has_fit and self.draw_fit:
self.func.Draw("same")
doubling, oneday = print_slope(self.func)
newpad = TPad("newpad","a transparent pad",0.15, 0.65, 0.4, 0.55);
self.text = TPaveText(0.1, 0.1, 0.9, 0.9)
self.text.AddText("Doubles every {} days".format(doubling))
self.text.AddText("Multiplies by {} every day".format(oneday))
newpad.SetFillStyle(4000);
newpad.Draw()
newpad.cd()
self.text.Draw()
# input()
def setup_graphics(self):
gStyle.SetOptStat(0)
gStyle.SetOptTitle(0)
gROOT.ForceStyle()
self.c = TCanvas("c", "c", 1920, 1080)
def make_hist(self, x, y, name, color):
gr = TH1D(name, name, len(x), x[0], x[-1])
for x_i, y_i, i in zip(x, y, range(1, len(x)+1)):
if y_i == 0.:
gr.SetBinContent(i, 0.)
else:
gr.SetBinContent(i, y_i + 1e-30)
gr.SetBinError(i, 0.05 * y_i)
# gr.Sumw2()
gr.GetXaxis().SetTimeDisplay(1)
gr.SetLineColor(color)
gr.SetMarkerStyle(21)
gr.SetMarkerSize(0.5)
gr.SetFillStyle(3002)
gr.SetFillColor(color)
gr.GetXaxis().SetNdivisions(16)
gr.GetXaxis().SetLabelSize(0.02)
return gr
def make_def(self):
self.hists = {
"confirmed" : self.hist_from_data("confirmed", self.fill_sandy),
"deaths" : self.hist_from_data("deaths", self.fill_redish),
"recovered" : self.hist_from_data("recovered", self.fill_greenish)
}
self.make_legend()
def make_legend(self):
l = TLegend(0.15, 0.85, 0.4, 0.7)
if self.country != "wo China":
l.SetHeader(self.country, "c")
else:
l.SetHeader("World (without Mainland China)", "c")
for name in self.hists:
l.AddEntry(self.hists[name], name)
self.legend = l
def get_data(self, data_type):
path = "./COVID-19/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_{}_global.csv".format(data_type)
my_data = genfromtxt(path, delimiter=',', dtype=str)
time_series_number = []
for line in my_data:
if line[1] == "Country/Region":
time_series_date = line
elif self.country == "wo China" and line[1] != "China":
time_series_number.append(line)
if line[1] == self.country:
time_series_number.append(line)
# if line[0] == "Hubei":
# time_series_number = line
cut = 4
time_series_date = time_series_date[cut:]
time_series_number = [time_series_number[i][cut:] for i in range(0, len(time_series_number))]
time_series_number = zip(*time_series_number)
y = array([list(map(float, i)) for i in time_series_number])
y = array(list(map(sum, y)))
x = array([date_to_ut(i) for i in time_series_date])
# print x
# x = x[(len(x) - len(y)):]
return x, y
def clear(self):
for key in self.hists:
self.hists[key].Delete()
try:
self.func.Delete()
except:
pass
self.c.Delete()
def do_floating_average(self, length):
for key in self.hists:
nBins = self.hists[key].GetNbinsX()
temp_length = length
for bin_i in range(1, nBins + 1):
if bin_i < length:
temp_length = bin_i
average_value = 0
total_error = 0
for float_i in range(bin_i - temp_length + 1, bin_i + 1):
average_value += self.hists[key].GetBinContent(float_i)
total_error += self.hists[key].GetBinError(float_i)**2
average_value /= temp_length
total_error = sqrt(total_error) / temp_length
self.hists[key].SetBinContent(bin_i, average_value)
self.hists[key].SetBinError(bin_i, total_error)
temp_length = length
return
def makePlot(trees,
cut,
variable,
combination,
name,
title=None,
datasetName="Data"):
from ROOT import TCanvas, TPad, TLegend, kBlue, kRed, TLatex
from src.ratios import RatioGraph
from src.histos import getHisto
from math import sqrt
from random import random
if title is None:
title = name
cut = cut.replace("inv", "p4.M()")
if "iso" in variable:
cut = cut.replace("id1 < 0.15", "id1 < 10")
cut = cut.replace("id2 < 0.15", "id2 < 10")
print cut
baseColor = kBlue
if "jzb" in name:
baseColor = kRed
rmue = 1.21
trigger = {"EE": 0.970, "EMu": 0.942, "MuMu": 0.964}
histos = {
"MuMu": getHisto(trees["MuMu"], cut, variable),
"EE": getHisto(trees["EE"], cut, variable),
"EMu": getHisto(trees["EMu"], cut, variable),
"EMu_MuLead": getHisto(trees["EMu"], "(%s) * (pt2 > pt1)" % cut,
variable),
}
rmueSys = 0
predictionSrc = "EMu"
if combination == "Both":
sameFlavour = histos["MuMu"].Clone("sameFlavourSum")
sameFlavour.Add(histos["EE"])
#~ nllPredictionScale = 0.5* sqrt(trigger["EE"]*trigger["MuMu"])*1./trigger["EMu"] *(rmue+1./(rmue))
#~ rmueSys = sqrt(sum(i**2 for i in [0.5*(1-(1./rmue**2))*0.1 + (1./trigger["EMu"] + 0.5*(1./trigger["EE"]+1./trigger["MuMu"]))*0.05]))
nllPredictionScale = 1.02
rmueSys = 0.07
#0.5*(1-1./rmue**2)*0.1
elif combination in ["EE", "MuMu"]:
rmueFactor = rmue
if combination == "EE":
rmueFactor = 1. / rmue
nllPredictionScale = 0.5 * sqrt(
trigger["EE"] * trigger["MuMu"]) * 1. / trigger["EMu"] * rmueFactor
rmueSys = sqrt(0.1**2 + (0.67 / 1.025)**2) * nllPredictionScale
sameFlavour = histos[combination].Clone("sameFlavourSum")
elif combination == "MuMuSF":
predictionSrc = "MuMu"
rmueFactor = 1. / rmue
nllPredictionScale = trigger["EE"] * 1. / trigger["MuMu"] * (rmueFactor
**2)
rmueSys = (2 * rmue) * 0.1
sameFlavour = histos["EE"].Clone("sameFlavourSum")
else:
raise StandardError, "unknown combination' %s'" % combination
prediction = histos[predictionSrc].Clone("ofPrediction")
predictionMuLead = histos["EMu_MuLead"].Clone("ofPrediction_MuLeading")
prediction.Scale(nllPredictionScale)
predictionMuLead.Scale(nllPredictionScale)
rooTex = {
"Both": "ee + #mu#mu",
"EE": "ee",
"MuMu": "#mu#mu",
"MuMuSF": "ee",
"EMu_Prediction": "OF-Prediciton",
"MuMu_Prediction": "SF(#mu#mu)-Prediciton"
}
canv = TCanvas("canv", "", 800, 800)
canv.Draw()
pad = TPad("main_%x" % (1e16 * random()), "main", 0.01, 0.25, 0.99, 0.99)
pad.SetNumber(1)
pad.Draw()
canv.cd()
resPad = TPad("residual_%x" % (1e16 * random()), "residual", 0.01, 0.01,
0.99, 0.25)
resPad.SetNumber(2)
resPad.Draw()
pad.cd()
#~ if "iso" in variable:
#~ canv.SetLogy(1)
#~ pad.SetLogy(1)
sameFlavour.SetMarkerStyle(20)
sameFlavour.SetLineColor(1)
sameFlavour.Draw("E P")
sameFlavour.SetTitle(title)
import ROOT
histos["MuMu"].SetLineColor(ROOT.kBlack)
histos["MuMu"].SetFillColor(ROOT.kWhite)
prediction.SetLineColor(1)
prediction.SetFillColor(baseColor - 7)
prediction.Draw("SAME Hist")
if predictionSrc == "EMu":
predictionMuLead.SetLineColor(1)
predictionMuLead.SetFillColor(baseColor - 9)
predictionMuLead.Draw("SAME Hist")
sameFlavour.Draw("E P SAME")
histos["MuMu"].Draw("SAME Hist")
pad.RedrawAxis()
canv.RedrawAxis()
leg = TLegend(0.75, 0.65, 1, 0.9)
leg.SetFillColor(0)
leg.AddEntry(sameFlavour, rooTex[combination], "P")
leg.AddEntry(histos["MuMu"], "#mu#mu events", "l")
leg.AddEntry(prediction, rooTex[predictionSrc + "_Prediction"], "F")
if predictionSrc == "EMu":
leg.AddEntry(predictionMuLead, "#mu leading", "F")
leg.Draw()
# print "_".join([name,variable]),
# print "EE",histos["EE"].GetEntries(), "MuMu:", histos["MuMu"].GetEntries(), "EMu:", histos["EMu"].GetEntries(),
# print " SF:", sameFlavour.GetEntries(), "OF:", prediction.GetEntries()
# tex = TLatex()
# tex.SetNDC()
# tex.SetTextSize()
# tex.DrawLatex(0.6, 0.7, title)
residuals = RatioGraph(sameFlavour, prediction)
residuals.addErrorBySize(rmueSys, rmueSys, add=False, color=kBlue - 9)
residuals.draw(resPad, yMin=0.5, yMax=1.5)
canv.Update()
variable = variable.replace("(", "").replace(")", "")
plotPath = "fig/%s.pdf" % ("_".join(
[datasetName, name, combination, variable]))
canv.Print(plotPath)
pad.Delete()
resPad.Delete()
canv.Delete()
# raw_input()
return plotPath
def main():
BIAS_DIR = global_paths.BIASDIR + args.btagging + "/"
if args.year == 'run2c':
BIAS_DIR += "combined_run2_signal{}/"
## individual plots stored in run2c_masspoints
## extract pulls
pulls = TGraph()
for m in range(1600, 8001, 100):
print
print
print "m = " + str(m)
for pull0 in [1, 3, 5, 8, 10, 15, 20, 50]:
print
print "r = " + str(pull0)
print "--------------------"
#try:
tfile = TFile.Open(
BIAS_DIR.format(pull0) +
"fitDiagnostics_M{mass}.root".format(mass=m), "READ")
tree = tfile.Get("tree_fit_sb")
## the method proposed in the documemtation
#hist = TH1D("hist", "hist", 20, -5, 5)
#tree.Project("hist", "(r-1)/(0.5*(rHiErr+rLoErr))")
#fit_func = TF1("gaussfit","gaus" , -5., 5.)
#hist.Fit(fit_func, "E")
#pulls.SetPoint(pulls.GetN(), m, fit_func.GetParameter(1)) ## get mean of gaussian fit
## Alberto's method
#pull0=PULL0[m]
#pull0=10.
hist = TH1D("s_pulls", ";%s/#sigma_{r};Number of toys" %
("(r - " + str(pull0) + ")"), 25, -5, +5) #
for i in range(tree.GetEntries()):
if hist.GetEntries() >= 1000: continue
tree.GetEntry(i)
#print "r = {} (+{}, -{})".format(tree.r, tree.rHiErr, tree.rLoErr)
##if tree.rLoErr < 0.: continue
if abs(tree.r + 1.) < 0.001: continue
if abs(tree.r - 1.) < 0.001: continue
if abs(tree.r - 0.) < 0.001: continue
#if abs(tree.rLoErr)>8.: continue # trying to skip these values FIXME
if tree.rHiErr == 0. or tree.rLoErr == 0.: continue
print "r = {} (+{}, -{})".format(tree.r, tree.rHiErr,
tree.rLoErr)
#pull = (tree.r-pull0)/(0.5*(abs(tree.rHiErr)+abs(tree.rLoErr))) ## documentation approach
#pull = (tree.r-pull0)/abs(tree.rHiErr) if tree.r-pull0 < 0. else (tree.r-pull0)/abs(tree.rLoErr) ## Alberto's sign convention
pull = (tree.r - pull0) / abs(
tree.rHiErr) if tree.r - pull0 > 0. else (
tree.r - pull0) / abs(tree.rLoErr) ## my own approach
#pull = (tree.r-pull0)/abs(tree.rHiErr) if tree.r < 0. else (tree.r-pull0)/abs(tree.rLoErr) ## Alberto's sign convention but depending directly on the sign of r
#pull = (tree.r-pull0)/abs(tree.rHiErr) if tree.r > 0. else (tree.r-pull0)/abs(tree.rLoErr) ## my own approach with an rErr dependence on r, not r-1
hist.Fill(pull)
### individual pltos for checking the fit quality
c1 = TCanvas("c1", "Pulls", 600, 600)
c1.cd()
c1.GetPad(0).SetTopMargin(0.06)
c1.GetPad(0).SetRightMargin(0.05)
c1.GetPad(0).SetBottomMargin(0.15)
c1.GetPad(0).SetTicks(1, 1)
#print "@ m= {}: \t mean = {}".format(m, hist.GetMean())
#pulls.SetPoint(pulls.GetN(), m, hist.GetMean()) ## get actual mean of histogram
fit_func = TF1("gaussfit", "gaus", -3., 3.)
##fit_func.SetParameter(1, hist.GetMean())
fit_func.SetParameter(1, 0.)
##fit_func.SetParLimits(1, -0.8, 0.8)
fit_func.SetParameter(2, 1.)
##fit_func.SetParameter(0, 45.)
##fit_func.SetParLimits(0, 30., 100.)
hist.Fit(fit_func, "E")
hist.Draw()
c1.Print("plots/bias/r_comparison/bias_fit_".format(m) + str(m) +
"_r" + str(pull0) + ".png")
pulls.SetPoint(
pulls.GetN(), m,
fit_func.GetParameter(1)) ## get fitted gaussian mean
hist.Delete()
c1.Delete()
tfile.Close()
def main():
gStyle.SetOptStat(0)
BIAS_DIR = global_paths.BIASDIR + args.btagging + "/"
if args.year == 'run2c':
BIAS_DIR += "combined_run2_r{}{}/"
## individual plots stored in run2c_masspoints
## extract pulls
pulls = {}
for signal_strength in ['0', '2sigma', '5sigma']:
print
print
print "--------------------------------------------------"
print "r = " + signal_strength
print "--------------------------------------------------"
pulls[signal_strength] = TGraphErrors()
for m in range(1600, 8001, 100):
if (signal_strength == '2sigma'
and m < 2600) or (signal_strength == '5sigma'
and m < 4100): ##FIXME FIXME
datacard_correction = True
else:
datacard_correction = False
print
print "m = " + str(m)
if datacard_correction:
print "correcting signal strength in the datacard"
print
pull0 = int(SIGNAL_STRENGTH[signal_strength][m])
tree = TChain("tree_fit_sb")
for seed in [
'123456', '234567', '345678', '456789', '567891', '678912',
'789123', '891234', '912345', '123459'
]:
tree.Add(
BIAS_DIR.format(signal_strength,
"_lowm" if datacard_correction else "") +
"fitDiagnostics_M{mass}_{seed}.root".format(
mass=m, seed=seed)) ##FIXME FIXME
hist = TH1D("s_pulls",
";%s/#sigma_{r};Number of toys" % ("#Deltar"), 25, -5,
+5) #
for i in range(tree.GetEntries()):
if hist.GetEntries() >= 1000: continue
tree.GetEntry(i)
#print "r = {} (+{}, -{})".format(tree.r, tree.rHiErr, tree.rLoErr)
##if tree.rLoErr < 0.: continue
if abs(tree.r + 1.) < 0.001: continue
if abs(tree.r - 1.) < 0.001: continue
if abs(tree.r - 0.) < 0.001: continue
if tree.rHiErr == 0. or tree.rLoErr == 0.: continue
if abs(tree.r + abs(tree.rHiErr) -
round(tree.r + abs(tree.rHiErr))) < 0.0001:
continue
if abs(tree.r - abs(tree.rLoErr) -
round(tree.r - abs(tree.rLoErr))) < 0.0001:
continue
#print "r = {} (+{}, -{})".format(tree.r, tree.rHiErr, tree.rLoErr)
pull = (tree.r - pull0) / abs(
tree.rHiErr) if tree.r - pull0 > 0. else (
tree.r - pull0) / abs(tree.rLoErr) ## my own approach
hist.Fill(pull)
## individual plots for checking the fit quality
c1 = TCanvas("c1", "Pulls", 600, 600)
c1.cd()
hist.GetXaxis().SetTitleSize(0.045)
hist.GetYaxis().SetTitleSize(0.045)
hist.GetYaxis().SetTitleOffset(1.1)
hist.GetXaxis().SetTitleOffset(1.05)
hist.GetXaxis().SetLimits(-6, 6.)
hist.GetYaxis().SetLimits(0, 200.)
hist.SetMinimum(0.)
hist.SetMaximum(190.)
c1.SetTopMargin(0.05)
##print "@ m= {}: \t mean = {}".format(m, hist.GetMean())
#pulls[signal_strength].SetPoint(pulls[signal_strength].GetN(), m, hist.GetMean()) ## get actual mean of histogram
fit_func = TF1("gaussfit", "gaus", -3., 3.)
hist.Fit(fit_func, "E")
hist.Draw()
drawCMS(-1, "Simulation Preliminary", year='run2')
drawMass("m_{Z'} = " + str(m) + " GeV")
c1.Print("plots/bias/run2c_masspoints/r" + signal_strength +
"/bias_fit_" + str(m) + "_" + args.year + ".pdf")
c1.Print("plots/bias/run2c_masspoints/r" + signal_strength +
"/bias_fit_" + str(m) + "_" + args.year + ".png")
n = pulls[signal_strength].GetN()
pulls[signal_strength].SetPoint(
n, m, fit_func.GetParameter(1)) ## get fitted gaussian mean
pulls[signal_strength].SetPointError(
n, 0., fit_func.GetParError(1)) ## set gaussian width as error
fit_func.Delete()
hist.Delete()
c1.Delete()
#except:
# print "something went wrong in m =", m
## draw pulls
outfile = TFile("plots/bias/bias_study_new_" + args.year + ".root",
"RECREATE")
c = TCanvas("canvas", "canvas", 800, 600)
leg = TLegend(0.65, 0.7, 0.95, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
for i, signal_strength in enumerate(['0', '2sigma', '5sigma']):
pulls[signal_strength].SetMarkerStyle(2)
pulls[signal_strength].SetMarkerColor(COLORS[signal_strength])
pulls[signal_strength].SetLineColor(COLORS[signal_strength])
pulls[signal_strength].SetLineWidth(2)
pulls[signal_strength].SetMinimum(-0.7)
pulls[signal_strength].SetMaximum(0.7)
pulls[signal_strength].Draw("APL" if i == 0 else "PL")
leg.AddEntry(pulls[signal_strength], LEGEND[signal_strength])
zeroline = TGraph()
zeroline.SetPoint(zeroline.GetN(), 1000, 0)
zeroline.SetPoint(zeroline.GetN(), 8600, 0)
zeroline.SetMarkerStyle(7)
zeroline.SetMarkerSize(0)
zeroline.SetLineStyle(15)
zeroline.SetLineColor(1)
zeroline.Draw("PL")
c.SetGrid()
pulls['0'].SetTitle(";m_{Z'} (GeV);mean #Deltar/#sigma_{r}")
pulls['0'].GetXaxis().SetTitleSize(0.045)
pulls['0'].GetYaxis().SetTitleSize(0.045)
pulls['0'].GetYaxis().SetTitleOffset(1.1)
pulls['0'].GetXaxis().SetTitleOffset(1.05)
pulls['0'].GetXaxis().SetLimits(1350., 8150.)
c.SetTopMargin(0.05)
leg.Draw()
drawCMS(-1, "Simulation Preliminary", year='run2')
c.Print("plots/bias/bias_study_new_" + args.year + ".png")
c.Print("plots/bias/bias_study_new_" + args.year + ".pdf")
c.Write()
outfile.Close()
Canv4Plots.Write()
CanvScurves.Close()
Canv4Plots.Close()
thresh1D.Delete()
thresh2D.Delete()
TDAC1D.Delete()
TDAC2D.Delete()
correlation.Delete()
sigma1D.Delete()
sigma2D.Delete()
chi2_1D.Delete()
chi2_2D.Delete()
eyeDiagram.Delete()
CanvScurves.Delete()
Canv4Plots.Delete()
# =====================================================================
# ----------- Analysis and suggestions of TDAC for every pixels
# =====================================================================
# Define function to set TDAC value and create a .txt file
file_TDAC = open(
"suggested_TDACfileTuningInterpolation402.txt", "w"
) # "r" instead of "w" in order to take the original suggested_TDACfile as an input instead of create TDACfile with all 6
file_TDAC_list = []
def main():
gStyle.SetOptStat(0)
BIAS_DIR = global_paths.BIASDIR+args.btagging+"/"
if args.year == 'run2c':
BIAS_DIR += "combined_run2/"
#BIAS_DIR += "combined_run2_signal{}/"
## individual plots stored in run2c_masspoints
## extract pulls
pulls = TGraphErrors()
for m in range(1600,8001,100):
#try:
pull0=int(PULL0[m])
#pull0=10.
#tfile = TFile.Open(BIAS_DIR+"fitDiagnostics_M{mass}.root".format(mass=m), "READ")
#tfile = TFile.Open(BIAS_DIR.format(pull0)+"fitDiagnostics_M{mass}.root".format(mass=m), "READ")
#tree = tfile.Get("tree_fit_sb")
tree = TChain("tree_fit_sb")
for seed in ['123456', '234567', '345678', '456789', '567891', '678912', '789123', '891234', '912345', '123459']:
tree.Add(BIAS_DIR+"fitDiagnostics_M{mass}_{seed}.root".format(mass=m, seed=seed))
## the method proposed in the documemtation
#hist = TH1D("hist", "hist", 20, -5, 5)
#tree.Project("hist", "(r-1)/(0.5*(rHiErr+rLoErr))")
#fit_func = TF1("gaussfit","gaus" , -5., 5.)
#hist.Fit(fit_func, "E")
#pulls.SetPoint(pulls.GetN(), m, fit_func.GetParameter(1)) ## get mean of gaussian fit
## Alberto's method
#hist = TH1D("s_pulls", ";%s/#sigma_{r};Number of toys" % ("(r - "+str(pull0)+")"), 25, -5, +5) #
hist = TH1D("s_pulls", ";%s/#sigma_{r};Number of toys" % ("#Deltar"), 25, -5, +5) #
for i in range(tree.GetEntries()):
if hist.GetEntries() >= 1000: continue
tree.GetEntry(i)
#print "r = {} (+{}, -{})".format(tree.r, tree.rHiErr, tree.rLoErr)
##if tree.rLoErr < 0.: continue
if abs(tree.r+1.) < 0.001: continue
if abs(tree.r-1.) < 0.001: continue
if abs(tree.r-0.) < 0.001: continue
#if abs(tree.rLoErr)>8.: continue # trying to skip these values FIXME
if tree.rHiErr==0. or tree.rLoErr==0.: continue
#print "r = {} (+{}, -{})".format(tree.r, tree.rHiErr, tree.rLoErr)
#pull = (tree.r-pull0)/(0.5*(abs(tree.rHiErr)+abs(tree.rLoErr))) ## documentation approach
#pull = (tree.r-pull0)/abs(tree.rHiErr) if tree.r-pull0 < 0. else (tree.r-pull0)/abs(tree.rLoErr) ## Alberto's sign convention
pull = (tree.r-pull0)/abs(tree.rHiErr) if tree.r-pull0 > 0. else (tree.r-pull0)/abs(tree.rLoErr) ## my own approach
#pull = (tree.r-pull0)/abs(tree.rHiErr) if tree.r < 0. else (tree.r-pull0)/abs(tree.rLoErr) ## Alberto's sign convention but depending directly on the sign of r
#pull = (tree.r-pull0)/abs(tree.rHiErr) if tree.r > 0. else (tree.r-pull0)/abs(tree.rLoErr) ## my own approach with an rErr dependence on r, not r-1
hist.Fill(pull)
## individual plots for checking the fit quality
c1 = TCanvas("c1", "Pulls", 600, 600)
c1.cd()
#c1.GetPad(0).SetTopMargin(0.06)
#c1.GetPad(0).SetRightMargin(0.05)
#c1.GetPad(0).SetBottomMargin(0.15)
#c1.GetPad(0).SetTicks(1, 1)
hist.GetXaxis().SetTitleSize(0.045)
hist.GetYaxis().SetTitleSize(0.045)
hist.GetYaxis().SetTitleOffset(1.1)
hist.GetXaxis().SetTitleOffset(1.05)
hist.GetXaxis().SetLimits(-5, 5.)
hist.GetYaxis().SetLimits(0, 200.)
hist.SetMinimum(0.)
hist.SetMaximum(190.)
c1.SetTopMargin(0.05)
##print "@ m= {}: \t mean = {}".format(m, hist.GetMean())
#pulls.SetPoint(pulls.GetN(), m, hist.GetMean()) ## get actual mean of histogram
fit_func = TF1("gaussfit","gaus" , -3., 3.)
###fit_func.SetParameter(1, hist.GetMean())
#fit_func.SetParameter(1, 0.)
###fit_func.SetParLimits(1, -0.8, 0.8)
#fit_func.SetParameter(2, 1.)
###fit_func.SetParameter(0, 45.)
###fit_func.SetParLimits(0, 30., 100.)
hist.Fit(fit_func, "E")
hist.Draw()
drawCMS(-1, "Simulation Preliminary", year='run2')
drawMass("m_{Z'} = "+str(m)+" GeV")
c1.Print("plots/bias/run2c_masspoints/bias_fit_"+str(m)+"_"+args.year+".pdf")
c1.Print("plots/bias/run2c_masspoints/bias_fit_"+str(m)+"_"+args.year+".png")
n = pulls.GetN()
pulls.SetPoint(n, m, fit_func.GetParameter(1)) ## get fitted gaussian mean
pulls.SetPointError(n, 0., fit_func.GetParError(1)) ## set gaussian width as error
hist.Delete()
c1.Delete()
#tfile.Close()
#except:
# print "something went wrong in m =", m
## draw pulls
c = TCanvas("canvas", "canvas", 800, 600)
pulls.SetTitle(";m_{Z'} (GeV);mean #Deltar/#sigma_{r}")
pulls.SetMarkerStyle(2)
pulls.SetMarkerColor(2)
pulls.SetLineColor(2)
pulls.SetLineWidth(2)
#pulls.GetYaxis().SetNdivisions(1020)
pulls.SetMinimum(-0.5)
pulls.SetMaximum(0.5)
pulls.Draw("APL")
zeroline = TGraph()
zeroline.SetPoint(zeroline.GetN(), 1000, 0)
zeroline.SetPoint(zeroline.GetN(), 8600, 0)
zeroline.SetMarkerStyle(7)
zeroline.SetMarkerSize(0)
zeroline.SetLineStyle(15)
zeroline.SetLineColor(1)
zeroline.Draw("PL")
c.SetGrid()
pulls.GetXaxis().SetTitleSize(0.045)
pulls.GetYaxis().SetTitleSize(0.045)
pulls.GetYaxis().SetTitleOffset(1.1)
pulls.GetXaxis().SetTitleOffset(1.05)
pulls.GetXaxis().SetLimits(1350., 8150.)
c.SetTopMargin(0.05)
drawCMS(-1, "Simulation Preliminary", year='run2')
c.Print("plots/bias/bias_study_"+args.year+".png")
c.Print("plots/bias/bias_study_"+args.year+".pdf")
c_mt1.cd()
histo[0].Draw("")
histo[0].SetLabelSize(0.07, "XY")
histo[0].GetXaxis().SetTitleOffset(3.)
histo[0].GetYaxis().SetTitleOffset(3.)
c_mt1.SaveAs("./plots/" + listofalgorithms[xi] + "_m" + listofnames[i] + "_pseudoexp_mt.pdf")
c_pull1.cd()
histo[2].Draw("")
histo[2].SetLabelSize(0.07, "XY")
histo[2].GetXaxis().SetTitleOffset(3.)
histo[2].GetYaxis().SetTitleOffset(3.)
c_pull1.SaveAs("./plots/" + listofalgorithms[xi] + "_m" + listofnames[i] + "_pseudoexp_pull.pdf")
c.Delete()
c.Close()
del histo
del histo_t
del histo_tup
del c
del c_mt1
del c_pull1
c2=TCanvas("total", "c2", 2000, 1000)
c2.Divide(2,1,0.01,0.01)
# fit
graph1.Fit("pol1")
