def makeTRTECCPlots(fp, fs, pulls):
c1 = TCanvas("c1", "c1", 971, 300)
c1.Divide(2, 1)
c1.cd(1)
h1 = (gDirectory.Get("trt_ec_residualR_Endcap_C")).Clone()
h1.SetTitle(fs("TRT Endcap C Residual R; Residual; Events"))
t = gaussfit(h1, True)
h1.UseCurrentStyle()
h1.Draw("HIST")
t[0].Draw("SAME")
c1.cd(2)
h2 = (gDirectory.Get("trt_ec_pullR_notube_Endcap_C")).Clone()
h2.SetTitle(fs("TRT Endcap C Pull; Pull; Events"))
t = gaussfit(h2, True)
h2.UseCurrentStyle()
h2.Draw("HIST")
t[0].SetRange(-2.5, 2.5)
t[0].Draw("SAME")
pulls["trtecC"] = t[1:]
c1.SaveAs(fp("trtECC.pdf"))
c1.SaveAs(fp("trtECC.png"))
c1.SaveAs(fp("trtECC.gif"))
c1.SaveAs(fp("trtECC.eps"))
def makeSCTECAPlots(fp, fs, pulls):
c1 = TCanvas("c1", "c1", 971, 300)
c1.Divide(2, 1)
c1.cd(1)
h1 = (gDirectory.Get("sct_eca_residualx")).Clone()
h1.SetTitle(fs("SCT Endcap A Residual X; Residual; Events"))
t = gaussfit(h1, True)
h1.UseCurrentStyle()
h1.Draw("HIST")
t[0].Draw("SAME")
c1.cd(2)
h2 = (gDirectory.Get("sct_eca_pullx")).Clone()
h2.SetTitle(fs("SCT Endcap A Pull X; Pull; Events"))
t = gaussfit(h2, True)
h2.UseCurrentStyle()
h2.Draw("HIST")
t[0].SetRange(-2.5, 2.5)
t[0].Draw("SAME")
pulls["sctecA"] = t[1:]
c1.SaveAs(fp("sctECA.pdf"))
c1.SaveAs(fp("sctECA.png"))
c1.SaveAs(fp("sctECA.gif"))
c1.SaveAs(fp("sctECA.eps"))
def plotStuff(plotList, plotstring, cutstring, plotfile, plotname, xlabel,
ylabel, unitnorm):
leg = TLegend(0.4, 0.75, 0.6, 0.9)
isFirst = True
for dataset in plotList:
dataset[0].Draw(plotstring.format(dataset[1]), cutstring, "goff")
hist = gDirectory.Get(dataset[1])
#hist.Sumw2()
if unitnorm:
hist.Scale(1.0 / hist.Integral())
else:
hist.Scale(1.0 / dataset[2])
print "{0} {1} {2}".format(plotname, dataset[4], hist.Integral())
hist.SetLineColor(dataset[3])
leg.AddEntry(hist, dataset[4])
if isFirst:
hist.GetXaxis().SetTitle(xlabel)
hist.GetYaxis().SetTitle(ylabel)
hist.Draw()
else:
hist.Draw("same")
isFirst = False
#leg.Draw()
data1Hist = gDirectory.Get("data1")
data2Hist = gDirectory.Get("data2")
data3Hist = gDirectory.Get("data3")
data1Hist.Divide(data3Hist)
data2Hist.Divide(data3Hist)
data1Hist.Draw()
data2Hist.Draw("same")
c.Print(plotfile, plotname)
def GetJonPlot(tree_gen, tree_fid, var, histbins, quantity='diff'):
#
# Quantity, or numerator
#
#jon_weight = '0.0000072*(event_type_truth<4)/20.277'
jon_weight = 'weight_fiducial*(event_type_truth<4)/20.277'
if quantity in ['fid', 'diff', 'eff']:
#jon_weight += '*(118.<=dressed_lep4_m && dressed_lep4_m<=129.)'
jon_weight += '*(118.<=bare_lep4_m && bare_lep4_m<=129.)'
the_tree = tree_fid
else:
the_tree = tree_gen
the_tree.Draw('%s>>h1%s' % (var, histbins), jon_weight, 'egoff')
h1 = gDirectory.Get('h1')
if quantity == 'eff':
#jon_weight = jon_weight.replace('*(118.<=dressed_lep4_m && dressed_lep4_m<=129.)','')
jon_weight = jon_weight.replace(
'*(118.<=bare_lep4_m && bare_lep4_m<=129.)', '')
tree_gen.Draw('%s>>Den%s' % (var, histbins), jon_weight, 'egoff')
Den = gDirectory.Get('Den')
h1.Divide(h1, Den, 1, 1, 'B')
gROOT.ProcessLine('delete Den')
if quantity == 'diff':
ConvertNeventsToDSigma(h1)
return h1
def get_nuis_corr(tfile, is_conditional, is_asimov, poi=1):
f = ROOT.TFile.Open(tfile)
mu = "1"
if is_asimov:
stub = "Asimov"
f.cd("PlotsAfterFitToAsimov")
mu = str(poi)
else:
stub = "Global"
f.cd("PlotsAfterGlobalFit")
mu = str(poi)
if is_conditional:
gDirectory.cd("conditionnal_MuIsEqualTo_" + mu)
#if is_asimov:
# gDirectory.cd("conditionnal_MuIsEqualTo_"+mu)
#else:
# gDirectory.cd("conditionnal_MuIsEqualTo_0")
#p_nuis = gDirectory.Get("can_NuisPara_"+stub+"Fit_conditionnal_mu0")
#p_corr = gDirectory.Get("can_CorrMatrix_"+stub+"Fit_conditionnal_mu0")
p_nuis = gDirectory.Get("can_NuisPara_" + stub +
"Fit_conditionnal_mu" + mu)
p_corr = gDirectory.Get("can_CorrMatrix_" + stub +
"Fit_conditionnal_mu" + mu)
else:
gDirectory.cd("unconditionnal")
p_nuis = gDirectory.Get("can_NuisPara_" + stub +
"Fit_unconditionnal_mu" + mu)
p_corr = gDirectory.Get("can_CorrMatrix_" + stub +
"Fit_unconditionnal_mu" + mu)
return p_nuis, p_corr
def draw_canvas_1d(name):
f_mc = TFile("plots/mc/%s_mcc7.root" % name)
print(f_mc.ls())
print(name)
h_mc = gDirectory.Get("h_%s_reco" % name)
h_mc.SetName("h_%s_reco" % name)
h_mc.SetLineColor(kRed + 1)
h_mc.SetFillColor(kRed + 1)
h_mc.SetFillStyle(0)
h_mc.GetYaxis().SetTitle("Efficiency")
h_mc.GetYaxis().SetRangeUser(0.61, 1.04)
f = TFile("plots/data/e_%s_pandoraCosmic.root" % name)
h = gDirectory.Get("h_%s" % name)
h.SetMarkerStyle(20)
h.SetLineColor(1)
f_sys = TFile("plots/data/e_%s_sys_pandoraCosmic.root" % name)
h_sys = gDirectory.Get("h_%s_sys" % name)
h_ratio = h_sys.Clone()
h_ratio.Divide(h_mc)
x_minbin = h_mc.FindFirstBinAbove()
low = h_mc.GetXaxis().GetBinLowEdge(x_minbin)
x_maxbin = h_mc.FindLastBinAbove()
high = h_mc.GetXaxis().GetBinUpEdge(x_maxbin)
h_mc.GetXaxis().SetRangeUser(low, high)
h_mc.GetYaxis().SetTitleSize(0.08)
h_mc.GetYaxis().SetLabelSize(0.065)
h_mc.GetYaxis().SetTitleOffset(0.59)
h_ratio.GetXaxis().SetRangeUser(low, high)
g_stat = histo2graph(h)
g_sys = histo2graph(h_sys)
g_sys.SetMarkerStyle(20)
g_sys.SetLineWidth(2)
g_stat.SetFillStyle(3001)
g_stat.SetFillColor(kGray + 2)
g_stat.SetLineWidth(2)
g_stat.SetLineColor(kGray + 2)
pt = TPaveText(0.09, 0.855, 0.32, 0.98, "ndc")
pt.AddText("MicroBooNE")
pt.SetFillColor(0)
pt.SetBorderSize(0)
pt.SetShadowColor(0)
leg = TLegend(0.55, 0.09, 0.86, 0.4)
leg.AddEntry(g_sys, "Data - stat. #oplus sys.", "ep")
leg.AddEntry(g_stat, "Data - stat. only", "f")
leg.AddEntry(h_mc, "Monte Carlo", "f")
canvas = draw_ratio("c_%s" % name, [h_mc, g_stat, g_sys, pt, leg],
[h_ratio], ["hist", "2", "p", "", ""], ["ep"])
canvas.SaveAs("plots/%s.pdf" % name)
canvas.SaveAs("plots/%s.C" % name)
def getResolution ( fileName ):
myfile = TFile( fileName )
mychain = gDirectory.Get( 'testTree' )
mychain.Draw('mainClustersEnergy>>tmpHist', '', 'goff')
hist = gDirectory.Get('tmpHist')
#print ( fileName )
#print ('nevent: %i ' % (entries))
return energyFit( hist )
def histdivider(tree, title, var, cut1, cut2):
"""
returns a histogram of the two histograms associated with the two
variables with their own cut
the returned hist will be hist1/hist2
"""
tree.Project('hist{t}'.format(t=title), var, cut1)
tree.Project('tmphist{t}'.format(t=title), var, cut2)
divided_hist = gDirectory.Get('hist{t}'.format(t=title))
divided_hist.Divide(gDirectory.Get('tmphist{t}'.format(t=title)))
divided_hist.SetTitle(title)
divided_hist.SetStats(False)
return divided_hist
def makeSCTBarrelPlots(fp, fs, pulls):
c1 = TCanvas("c1", "c1", 971, 300)
c1.Divide(2, 1)
c1.cd(1)
h1 = (gDirectory.Get("sct_b0_residualx")).Clone()
h1.SetName("sct_b_residualx")
h1.Add(gDirectory.Get("sct_b1_residualx"))
h1.Add(gDirectory.Get("sct_b2_residualx"))
h1.Add(gDirectory.Get("sct_b3_residualx"))
h1.SetTitle(fs("SCT Barrel Residual X; Residual; Events"))
t = gaussfit(h1, True)
h1.UseCurrentStyle()
h1.Draw("HIST")
t[0].Draw("SAME")
c1.cd(2)
h2 = (gDirectory.Get("sct_b0_pullx")).Clone()
h2.SetName("sct_b_pullx")
h2.Add(gDirectory.Get("sct_b1_pullx"))
h2.Add(gDirectory.Get("sct_b2_pullx"))
h2.Add(gDirectory.Get("sct_b3_pullx"))
h2.SetTitle(fs("SCT Barrel Pull X; Pull; Events"))
t = gaussfit(h2, True)
h2.UseCurrentStyle()
h2.Draw("HIST")
t[0].SetRange(-2.5, 2.5)
t[0].Draw("SAME")
pulls["sctbar"] = t[1:]
c1.SaveAs(fp("sctBarrel.pdf"))
c1.SaveAs(fp("sctBarrel.png"))
c1.SaveAs(fp("sctBarrel.gif"))
c1.SaveAs(fp("sctBarrel.eps"))
def draw_canvas_2d(name):
f_mc = TFile("plots/mc/%s_mcc7.root" % name)
h_mc = gDirectory.Get("h_%s_reco" % name)
f = TFile("plots/data/e_%s_pandoraCosmic.root" % name)
h = gDirectory.Get("h_%s" % name)
h.Divide(h_mc)
h.GetZaxis().SetRangeUser(0.5, 1.5)
#h.GetZaxis().SetTitle("Data/Monte Carlo")
#h.GetZaxis().RotateTitle()
h.SetMarkerSize(2)
x_minbin = h.FindFirstBinAbove(0, 1)
low_x = h.GetXaxis().GetBinLowEdge(x_minbin)
x_maxbin = h.FindLastBinAbove(0, 1)
high_x = h.GetXaxis().GetBinUpEdge(x_maxbin)
y_minbin = h.FindFirstBinAbove(0, 2)
low_y = h.GetYaxis().GetBinLowEdge(y_minbin)
y_maxbin = h.FindLastBinAbove(0, 2)
high_y = h.GetYaxis().GetBinUpEdge(y_maxbin)
h.GetXaxis().SetRangeUser(low_x, high_x)
h.GetYaxis().SetRangeUser(low_y, high_y)
c = TCanvas("c_%s" % name)
h.SetMarkerSize(2.5)
gPad.SetBottomMargin(0.17)
gPad.SetLeftMargin(0.13)
gPad.SetTopMargin(0.15)
gPad.SetRightMargin(0.15)
h.GetYaxis().SetTitleSize(0.07)
h.GetXaxis().SetTitleSize(0.07)
h.GetYaxis().SetTitleOffset(0.8)
pt = TPaveText(0.13, 0.855, 0.42, 0.98, "ndc")
pt.AddText("MicroBooNE")
pt.SetFillColor(0)
pt.SetBorderSize(0)
pt.SetShadowColor(0)
h.Draw("colz texte")
pt.Draw()
c.Update()
c.SaveAs("plots/%s.pdf" % name)
c.SaveAs("plots/%s.p" % name)
return c
def calc_delta_stats_same_tracks(run_id, use_wrapping=True):
"""Calculates differences in orientation of fields when tracks are the same"""
hit_trees = []
root_files = []
for is_parallel in [True, False]:
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkglobal.results_dir + "Results_RID" + str(
run_id) + "_" + letter + ".root"
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return {}
root_files.append(TFile(file_path, "READ"))
hit_trees.append(gDirectory.Get('hitTree'))
gROOT.cd()
delta_phi_list = []
for i in xrange(hit_trees[0].GetEntries()):
hit_trees[0].GetEntry(i)
hit_trees[1].GetEntry(i)
delta_phi = hit_trees[0].phi - hit_trees[1].phi
delta_phi_list.append(delta_phi)
if use_wrapping:
delta_phi_mean = srkmisc.reduce_periodics(delta_phi_list)
else:
delta_phi_mean = srkmisc.careful_mean(delta_phi_list)
delta_phi_std = srkmisc.careful_std(delta_phi_list)
root_files[0].Close()
root_files[1].Close()
return [delta_phi_mean, delta_phi_std]
def make_alpha_vs_phi_plot(run_id, is_parallel):
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(
run_id) + "_" + letter + ".root"
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return {}
root_file = TFile(file_path, "READ")
hit_tree = gDirectory.Get('hitTree')
gROOT.cd()
num_events = hit_tree.GetEntries()
phi_list = []
alpha_list = []
radius = srkdata.get_data_for_rids_from_database([run_id],
"ChamberRadius")[0][0]
print radius
for i in xrange(num_events):
hit_tree.GetEntry(i)
phi_list.append(hit_tree.phi)
alpha = get_alpha_angle_2d(radius, hit_tree.pos0, hit_tree.vel0)
alpha_list.append(alpha)
root_file.Close()
phi_mean = srkmisc.reduce_periodics(phi_list)
delta_phi_list = map(lambda x: x - phi_mean, phi_list)
plt.scatter(alpha_list, delta_phi_list)
def addRuns(self):
# Read summary histogram
hin = gDirectory.Get('readyMuDist')
if hin == None:
print 'No mu histogram found for run', run
return
hin.Print()
# Check bin 1
bin1 = hin.GetBinContent(1)
if bin1 > 100:
print '==> Zero bin: %f' % bin1
if self.hout == None:
print 'Creating new output histogram'
self.out = TFile(self.outFile, 'recreate')
self.hout = TH1D(hin)
self.hout.SetName(self.houtName)
else:
self.hout.Add(hin)
return
def get_result_data(leaf_names, run_id, is_parallel):
"""Get result data directly from root files based on leaf names"""
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(
run_id) + "_" + letter + ".root"
print "Opening ", file_path
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return {}
root_file = TFile(file_path, "READ")
hit_tree = gDirectory.Get('hitTree')
gROOT.cd()
num_events = hit_tree.GetEntries()
data = []
for leaf_name in leaf_names:
temp_data = []
for i in xrange(num_events):
hit_tree.GetEntry(i)
temp_data.append(
hit_tree.GetBranch(leaf_name).GetListOfLeaves()[0].GetValue())
data.append(temp_data)
root_file.Close()
return data
def splitTree():
dsNum, subNum = 0, 25
inPath = "/global/homes/w/wisecg/project/waveskim/waveSkimDS%d_%d.root" % (dsNum, subNum)
outPath = "./cutSkimDS%d_%d.root" % (dsNum, subNum)
inFile = TFile(inPath)
bigTree = inFile.Get("skimTree")
theCut = inFile.Get("theCut").GetTitle()
bigTree.Draw(">>elist",theCut,"entrylist")
elist = gDirectory.Get("elist")
bigTree.SetEntryList(elist)
nList = elist.GetN()
outFile = TFile(outPath,"RECREATE")
lilTree = TTree()
lilTree.SetMaxTreeSize(150000000)
lilTree = bigTree.CopyTree("")
lilTree.Write("",TObject.kOverwrite)
thisCut = TNamed("theCut",theCut)
thisCut.Write()
def make_txt_hist_from_root_file(file_path_txt,
rid,
is_parallel,
draw_string,
hist_dim,
cut_string="",
option_string="",
titles=("", "", "")):
"""Given a draw/cut string, take an SRK ROOT results file and create a histogram from it."""
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(
rid) + "_" + letter + ".root"
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return
root_file = TFile(file_path, "READ")
hit_tree = gDirectory.Get('hitTree')
gROOT.cd()
histogram = TH1D("tempHist", "tempHist", hist_dim[0], hist_dim[1],
hist_dim[2])
total_draw_string = draw_string + " >> tempHist"
hit_tree.Draw(total_draw_string, cut_string, option_string)
make_txt_from_hist(file_path_txt, histogram, titles)
root_file.Close()
return
def make_root_plot_from_results_file(rid,
is_parallel,
draw_string,
cut_string="",
option_string=""):
"""Makes a ROOT plot from results file based on a draw and cut string."""
# gStyle.SetOptStat("iMRKS")
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkglobal.results_dir + "Results_RID" + str(
rid) + "_" + letter + ".root"
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return
root_file = TFile(file_path, "READ")
hit_tree = gDirectory.Get('hitTree')
gROOT.cd()
c1 = rootnotes.canvas("Canvas", (1200, 800))
hit_tree.Draw(draw_string, cut_string, option_string)
root_file.Close()
return c1
def fillTree(self, name, event, specific = None):
#determine if we need all event info or only part
keys = event.keys()
if specific is not None:
keys = specific
#if we haven't made the tree, create it and set it up
if name not in self.myTrees.keys():
print 'creating tree %s'%name
self.myTrees[name] = TTree(name,name)
gDirectory.Add(self.myTrees[name])
self.myTrees[name] = gDirectory.Get(name)
#create the struct we bind to our branches
stct = self._makeStruct(name,keys,event)
if len(stct) > 0:
gROOT.ProcessLine('.L %s_vars.C'%(name))
else:
gROOT.ProcessLine(stct)
gROOT.ProcessLine('%s_vars %s_vars_holder;'%(name,name))
self.myStructs[name] = getattr(ROOT,"%s_vars"%(name))()
#bind branches to the members of the struct we've created
self._bindBranches(name,keys,event)
self.myTrees[name].SetCacheSize()
#we are now done setting up the tree (if needed) -> set values and fill
if isinstance(event,dict):
for key in keys:
if hasattr(self.myStructs[name],key):
setattr(self.myStructs[name],key,event[key])
else:
self.myDicts[name][key] = event[key]
self.myTrees[name].SetBranchAddress(key,
self.myDicts[name][key])
self.myTrees[name].Fill()
def setEventList(cut=None):
print 'now browsing the full tree... might take a while, but drawing will then be faster!'
events.Draw('>>pyplus', cut)
from ROOT import gDirectory
pyplus = gDirectory.Get('pyplus')
pyplus.SetReapplyCut(True)
events.SetEventList(pyplus)
def setEventList(tree, cut=None):
'''to undo, call tree.SetEventList(0)'''
print 'now browsing the full tree... might take a while, but drawing will then be faster!'
tree.Draw('>>pyplus', cut)
pyplus = gDirectory.Get('pyplus')
pyplus.SetReapplyCut(True)
tree.SetEventList(pyplus)
def makeIBLPlots(fp, fs, pulls):
c1 = TCanvas("c1", "c1", 971, 600)
c1.Divide(2, 2)
c1.cd(1)
h1 = (gDirectory.Get("pix_b0_residualx")).Clone()
h1.SetTitle(fs("IBL Residual X; Residual; Events"))
t = gaussfit(h1, False)
h1.UseCurrentStyle()
h1.Draw("HIST")
t[0].Draw("SAME")
c1.cd(2)
h2 = (gDirectory.Get("pix_b0_pullx")).Clone()
h2.SetTitle(fs("IBL Pull X; Pull; Events"))
t = gaussfit(h2, True)
h2.UseCurrentStyle()
h2.Draw("HIST")
t[0].SetRange(-2.5, 2.5)
t[0].Draw("SAME")
pulls["iblx"] = t[1:]
c1.cd(3)
h3 = (gDirectory.Get("pix_b0_residualy")).Clone()
h3.SetTitle(fs("IBL Residual Y; Residual; Events"))
t = gaussfit(h3, False)
h3.UseCurrentStyle()
h3.Draw("HIST")
t[0].Draw("SAME")
c1.cd(4)
h4 = (gDirectory.Get("pix_b0_pully")).Clone()
h4.SetTitle(fs("IBL Pull Y; Pull; Events"))
t = gaussfit(h4, True)
h4.UseCurrentStyle()
h4.Draw("HIST")
t[0].SetRange(-2.5, 2.5)
t[0].Draw("SAME")
pulls["ibly"] = t[1:]
c1.SaveAs(fp("IBL.pdf"))
c1.SaveAs(fp("IBL.png"))
c1.SaveAs(fp("IBL.gif"))
c1.SaveAs(fp("IBL.eps"))
def getCovarianceElement(self, var1, var2):
celement = 0.0
#Don't want to output the draws
gROOT.SetBatch(True)
self.rchain.Draw(var1+">>var1mean")
v1h = gDirectory.Get("var1mean")
var1mean = v1h.GetMean()
self.rchain.Draw(var2+">>var2mean")
v2h = gDirectory.Get("var2mean")
var2mean = v2h.GetMean()
for i in xrange(self.rchain.GetEntries()):
self.rchain.GetEntry(i)
v1 = float(getattr(self.rchain,var1))
v2 = float(getattr(self.rchain,var2))
celement = celement + ((v1 - var1mean)*(v2-var2mean))
Normalization = 1.0/(float(self.rchain.GetEntries())-1.0)
return Normalization*celement
def retrieveHistogram(inputFile, h_Path, h_Name):
h_file = TFile(inputFile)
gDirectory.cd(h_Path)
h = gDirectory.Get(h_Name)
h.SetDirectory(0)
return h
def ScaleToInt(histos, varControl):
for sample in histos:
histo = gDirectory.Get(histos[sample])
#histo=histos[sample]
norm = histo.GetSumOfWeights()
if (norm < 0.0001): norm = 1.0
if varControl is False: histo.Scale(1 / norm)
if norm < 10: histo.Scale(0.1)
def findYrange(hdict, scaleFactor):
yMax = 0
for samp in hdict:
histo = gDirectory.Get(hdict[samp])
histo.Rebin(scaleFactor)
ymaxBin = histo.GetMaximumBin()
yMax = max(yMax, histo.GetBinContent(ymaxBin))
return yMax
def ReadRootFile(self, RootFileDir, bool=False):
"""HVMapNoise.ReadRootFile(string RootFileDir[, bool bool]) Reads in the path of a .root file of pedestal run noise and stores it in self.noise. If bool is true then data taken in peak mode is multiplied by 1.7"""
#make varibles to keep track of dictionary keys
RunT = RootFileDir.split('/')[-1].split('-')[1]
SubD = RootFileDir.split('/')[-1].split('-')[2]
#intialize varible to record the number of entries read from RootFile
Ndet = 0
# Open the file:
myfile = TFile(RootFileDir, 'r')
# retrieve the ntuple of interest
mychain = gDirectory.Get('tree')
entries = mychain.GetEntriesFast()
#Loop over the trees entries
for jentry in xrange(entries):
#get the tree from the chain
ientry = mychain.LoadTree(jentry)
#copy the entry in to memory and verify
nb = mychain.GetEntry(jentry)
#use the values directly from the tree
detID = str(mychain.DETID)
APVID = int(mychain.APVID)
NoiseMean = mychain.NOISEMEAN
#compensate for effects of detector DAQ mode on noise means
if bool:
if 'PEAK' in RootFileDir:
NoiseMean = NoiseMean * 1.7
#Add to dictionary and check for detID/APVID uniqueness
if RunT not in self.Noise.keys():
self.Noise.update({RunT: {SubD: {detID: {APVID: NoiseMean}}}})
Ndet = Ndet + 1
elif SubD not in self.Noise[RunT].keys():
self.Noise[RunT].update({SubD: {detID: {APVID: NoiseMean}}})
Ndet = Ndet + 1
elif detID not in self.Noise[RunT][SubD].keys():
self.Noise[RunT][SubD].update({detID: {APVID: NoiseMean}})
Ndet = Ndet + 1
else:
if APVID not in self.Noise[RunT][SubD][detID].keys():
self.Noise[RunT][SubD][detID].update({APVID: NoiseMean})
else:
print "Warning: in year: ", Year, ',', " detID: ", RunT, SubD, detID, " has found two noise mean entries for APVID: ", APVID, " could indicate file copy/naming error!"
myfile.Close()
print "Done reading in ", Ndet, " detIDs from", RootFileDir.split(
'/')[-1]
def plotstuff(var, cut, maxsig, plotname):
c.Clear()
c.Divide(1, 2)
c.cd(1)
events.Draw(var, cut, "colz")
hnew = gDirectory.Get("hnew")
hnew.FitSlicesY()
hnew.SetTitle(plotname)
hnew.GetXaxis().SetTitle("momentum [GeV]")
hnew.GetYaxis().SetTitle("DOCA [mm]")
hnew_1 = gDirectory.Get("hnew_2")
c.cd(2)
hnew_1.Draw()
hnew_1.Fit("fitfunc", "", "", 0.2, 0.75)
hnew_1.GetYaxis().SetRangeUser(0, maxsig)
hnew_1.GetXaxis().SetTitle("momentum [GeV]")
hnew_1.GetYaxis().SetTitle("DOCA resolution [mm]")
c.Print(remainder[0] + ".pdf", "Title:top_yz")
def drawAndPrint(var, altname=''):
if len(altname) < 1:
altname = var
data.Draw(var + '>>' + altname + '(30)', var + ' != -9999.99', 'PE')
h = gDirectory.Get(altname)
h.SetMarkerStyle(20)
if (altname[-5:] == '_bias') and (altname[0] == 'n'):
print altname, 'mean:', h.GetMean()
if (altname[-5:] == '_pull') and (opts.fitPull):
fitfunc = 'gaus(0)/sqrt(TMath::TwoPi())/[2]'
gfit = TF1('gfit', fitfunc,
h.GetXaxis().GetXmin(),
h.GetXaxis().GetXmax())
gfit.SetParameters(1.0, h.GetMean(), h.GetRMS())
gfit.FixParameter(0, 1.0)
data.UnbinnedFit('gfit', var, '', 'E')
gfit2 = TF1('gfit2', fitfunc,
h.GetXaxis().GetXmin(),
h.GetXaxis().GetXmax())
gfit2.SetParameter(0, 1.0)
gfit2.FixParameter(1, gfit.GetParameter(1))
gfit2.FixParameter(2, gfit.GetParameter(2))
h.Fit('gfit2', 'LN0Q')
gfit.FixParameter(0, gfit2.GetParameter(0))
L = TLatex()
L.SetNDC()
L.SetTextSize(0.04)
L.SetTextAlign(12)
h.Draw('PE')
gfit.Draw('same')
L.DrawLatex(0.12, 0.85, "<x>: %4.3f #pm %4.3f" % \
(gfit.GetParameter(1), gfit.GetParError(1)))
L.DrawLatex(0.12, 0.8, "#sigma_{x}: %4.3f #pm %4.3f" % \
(gfit.GetParameter(2), gfit.GetParError(2)))
c.Modified()
sums = ''
errs = ''
errstr = ''
trues = ''
if ((altname[:4] == 'nsig') or (altname[:5] == 'nbkgd')) and \
(altname[-4:] != '_err') and (altname[-5:] != '_pull') and \
(altname[-5:] != '_bais'):
mean = h.GetMean()
rms = h.GetRMS()
errstr = '%s mean: %5.3f rms: %5.3f fractional err: %6.4f' % (
altname, mean, rms, rms / mean)
#print errstr
#if (var == altname):
sums = altname + '+'
errs = (altname + '_err*') * 2
errs = errs[:-1] + '+'
trues = altname + '_true+'
c.Update()
c.Print(opts.plotdir + '/' + altname + '.png')
c.Print(opts.plotdir + '/' + altname + '.pdf')
return errstr, sums, errs, trues
def findBeamCaloCoords(data):
from ROOT import gDirectory
data.Draw('maxEtaHB>>maxEtaHB(16,0.5,16.5)', 'triggerID==4', 'goff')
temph = gDirectory.Get('maxEtaHB')
ieta = findBeamCenter(temph)
data.Draw('maxEtaEB>>maxEtaEB(50,0.5,50.5)', 'triggerID==4', 'goff')
temph = gDirectory.Get('maxEtaEB')
xtalieta = findBeamCenter(temph)
data.Draw('maxPhiHB>>maxPhiHB(10,0.5,10.5)', 'triggerID==4', 'goff')
temph = gDirectory.Get('maxPhiHB')
iphi = findBeamCenter(temph)
data.Draw('maxPhiEB>>maxPhiEB(21,0.5,21.5)', 'triggerID==4', 'goff')
temph = gDirectory.Get('maxPhiEB')
xtaliphi = findBeamCenter(temph)
return (int(ieta), int(iphi), int(xtalieta), int(xtaliphi))
def buildArraysFromROOT(tree, allowedFeatures, cut, skipEvents, maxEvents,
name):
dataContainer = {}
featureNames = []
eventCounter = -1
gROOT.Reset()
# Get branch names
for item in tree.GetListOfBranches():
featureName = item.GetName()
if featureName in allowedFeatures:
featureNames.append(featureName)
dataContainer[featureName] = []
# Build the event list
tcut = TCut(cut)
tree.Draw(">>eventList", tcut)
eventList = TEventList()
eventList = gDirectory.Get("eventList")
nSelectedEvents = eventList.GetN()
# Event loop
for i in range(0, nSelectedEvents):
if (i < skipEvents):
continue
if (i % 100 == 0):
sys.stdout.write("Reading %s: %d%% \r" %
(tree.GetName(), 100 * i /
(maxEvents + skipEvents)))
sys.stdout.flush()
if i >= (maxEvents + skipEvents):
break
selectedEvNum = eventList.GetEntry(i)
tree.GetEntry(selectedEvNum)
for feature in featureNames:
dataContainer[feature].append(tree.__getattr__(feature))
sys.stdout.write("\n")
# Make the numpy arrays
outputArray = np.array([])
for feature in allowedFeatures:
column = dataContainer[feature]
feature_vector = np.asarray(column)
feature_vector = feature_vector.reshape(feature_vector.size, 1)
if outputArray.shape[0] == 0:
outputArray = feature_vector
else:
outputArray = np.concatenate((outputArray, feature_vector), axis=1)
imp = Imputer(missing_values=-999, strategy='mean', axis=0)
imp.fit(outputArray)
outputArray = imp.transform(outputArray)
print name
print "Events: ", outputArray.shape[0]
print "Features: ", outputArray.shape[1]
return outputArray
