def testIthr():
lines = get_lines('DAC_scan_ithr_0x40to0xf0.dat')
gr1 = TGraphErrors()
gr2 = TGraphErrors()
fUnit = 1000. / 0.7
yUnit = 'e^{-}'
for line in lines:
if len(line) == 0: continue
if line[0] in ['#', '\n']: continue
fs = line.rstrip().split()
ix = int(fs[0])
gr1.SetPoint(ix, float(fs[1]), float(fs[2]) * fUnit)
gr1.SetPointError(ix, 0, float(fs[3]) * fUnit)
gr2.SetPoint(ix, float(fs[1]), float(fs[4]) * fUnit)
gr2.SetPointError(ix, 0, float(fs[5]) * fUnit)
useAtlasStyle()
gStyle.SetMarkerStyle(20)
gr1.SetMarkerStyle(20)
gr1.Draw('AP')
h1 = gr1.GetHistogram()
h1.GetYaxis().SetTitle("Threshold [" + yUnit + "]")
h1.GetXaxis().SetTitle("I_{Thre} code")
# h1.GetYaxis().SetRangeUser(0,0.2)
gPad.SetTicks(1, 0)
gPad.SetRightMargin(0.16)
y1b = 0
y2b = 15
x1 = h1.GetXaxis().GetXmax()
y1 = h1.GetYaxis().GetXmin()
y2 = h1.GetYaxis().GetXmax()
raxis = TGaxis(x1, y1, x1, y2, y1b, y2b, 506, "+L")
raxis.SetLineColor(2)
raxis.SetLabelColor(2)
raxis.SetTitleColor(2)
raxis.SetTitle("ENC [" + yUnit + "]")
raxis.Draw()
nP = gr2.GetN()
Ys = gr2.GetY()
EYs = gr2.GetEY()
Y = array(
'd', [y1 + (y2 - y1) / (y2b - y1b) * (Ys[i] - y1b) for i in range(nP)])
EY = array('d', [(y2 - y1) / (y2b - y1b) * EYs[i] for i in range(nP)])
gr2x = TGraphErrors(nP, gr2.GetX(), Y, gr2.GetEX(), EY)
gr2x.SetMarkerStyle(24)
gr2x.SetLineColor(2)
gr2x.SetMarkerColor(2)
gr2x.Draw('Psame')
waitRootCmdX()
def get_rank_section(directory):
# do Rank histo png
imgname = "RankSummary.png"
gStyle.SetPadTickY(0)
c = TCanvas("ranks", "ranks", 500, 400)
#gStyle.SetOptStat(0)
c.cd()
h = directory.rank_histo
rank_histof = TH1F(h.GetName(), "", h.GetNbinsX(),
h.GetXaxis().GetXmin(),
h.GetXaxis().GetXmax())
rank_histof.SetLineWidth(2)
for i in xrange(0, h.GetNbinsX() + 1):
rank_histof.SetBinContent(i, h.GetBinContent(i))
h.SetTitle("Ranks Summary;Rank;Frequency")
h.Draw("Hist")
c.Update()
rank_histof.ComputeIntegral()
integral = rank_histof.GetIntegral()
rank_histof.SetContent(integral)
rightmax = 1.1 * rank_histof.GetMaximum()
scale = gPad.GetUymax() / rightmax
rank_histof.SetLineColor(kRed)
rank_histof.Scale(scale)
rank_histof.Draw("same")
#draw an axis on the right side
axis = TGaxis(gPad.GetUxmax(), gPad.GetUymin(), gPad.GetUxmax(),
gPad.GetUymax(), 0, rightmax, 510, "+L")
axis.SetTitle("Cumulative")
axis.SetTitleColor(kRed)
axis.SetLineColor(kRed)
axis.SetLabelColor(kRed)
axis.Draw()
rank_histof.Draw("Same")
c.Print(imgname)
page_html = '<div class="span-20"><h2 class="alt"><a name="rank_summary">Ranks Summary</a></h2>'
page_html += '<div class="span-19"><img src="%s"></div>' % imgname
page_html += '</div> <a href="#top">Top...</a><hr>'
return page_html
gr_minus.SetLineWidth(4)
gr_minus.SetMarkerSize(3)
gr_minus.SetMarkerStyle(21)
gr_minus.SetMarkerColor(4)
gr_plus.Draw("ep same")
gr_minus.Draw("ep same")
canvas.Update()
axis = TGaxis(ROOT.gPad.GetUxmax(), ROOT.gPad.GetUymin(),
ROOT.gPad.GetUxmax(), ROOT.gPad.GetUymax(), 0, 2,
510, "+L")
axis.SetTitle("Temperature (before - after) [degC]")
axis.SetTitleOffset(1.5)
axis.SetTitleFont(42)
axis.SetLineColor(6)
axis.SetTextColor(6)
axis.SetLabelFont(42)
axis.Draw()
gr_temp.SetLineColor(6)
gr_temp.SetLineWidth(4)
gr_temp.SetMarkerSize(3)
gr_temp.SetMarkerStyle(22)
gr_temp.SetMarkerColor(6)
label = ROOT.TLatex(0.68, 0.75, klayer)
label.SetNDC()
label2 = ROOT.TLatex(0.2, 0.93, "CMS")
label2.SetNDC()
c_allmases.cd(3)
ymax = 1.
graph_pratio_allmases.Draw("AP")
graph_pratio_allmases.GetXaxis().SetTitle("Nr of tracks")
graph_pratio_allmases.GetYaxis().SetTitle("<ratio>")
graph_pratio_allmases.GetYaxis().SetRangeUser(0., ymax)
graph_pratio_allmases.Draw("AP")
graph_ntr_allmases.Draw("*")
leg_all = TLegend(0.65, 0.35, 0.9, 0.55)
leg_all.AddEntry(graph_pratio_allmases, "pratio", "l")
leg_all.AddEntry(graph_ntr_allmases, "number of tracks", "p")
leg_all.SetTextSize(0.015)
leg_all.Draw()
axis = TGaxis(10.8, 0., 10.8, ymax, 0, ymax * rightmax / leftmax, 510, "+L")
axis.SetLineColor(2)
axis.SetLabelColor(2)
axis.Draw()
c_allmases.cd(4)
zmax = 0.07
graph_deltar_allmases.Draw("AP")
graph_deltar_allmases.GetXaxis().SetTitle("Nr of tracks")
graph_deltar_allmases.GetYaxis().SetTitle("deltaR")
graph_deltar_allmases.GetYaxis().SetRangeUser(0., zmax)
graph_deltar_allmases.Draw("AP")
graph_ntr_allmases2.Draw("*")
axis2 = TGaxis(10.8, 0., 10.8, zmax, 0, zmax * rightmax2 / leftmax2, 510, "+L")
axis2.SetLineColor(2)
axis2.SetLabelColor(2)
def train_and_apply():
np.random.seed(1)
ROOT.gROOT.SetBatch()
#Extract data from root file
tree = uproot.open("out_all.root")["outA/Tevts"]
branch_mc = [
"MC_B_P", "MC_B_eta", "MC_B_phi", "MC_B_pt", "MC_D0_P", "MC_D0_eta",
"MC_D0_phi", "MC_D0_pt", "MC_Dst_P", "MC_Dst_eta", "MC_Dst_phi",
"MC_Dst_pt", "MC_Est_mu", "MC_M2_miss", "MC_mu_P", "MC_mu_eta",
"MC_mu_phi", "MC_mu_pt", "MC_pis_P", "MC_pis_eta", "MC_pis_phi",
"MC_pis_pt", "MC_q2"
]
branch_rec = [
"B_P", "B_eta", "B_phi", "B_pt", "D0_P", "D0_eta", "D0_phi", "D0_pt",
"Dst_P", "Dst_eta", "Dst_phi", "Dst_pt", "Est_mu", "M2_miss", "mu_P",
"mu_eta", "mu_phi", "mu_pt", "pis_P", "pis_eta", "pis_phi", "pis_pt",
"q2"
]
nvariable = len(branch_mc)
x_train = tree.array(branch_mc[0], entrystop=options.maxevents)
for i in range(1, nvariable):
x_train = np.vstack(
(x_train, tree.array(branch_mc[i], entrystop=options.maxevents)))
x_test = tree.array(branch_rec[0], entrystop=options.maxevents)
for i in range(1, nvariable):
x_test = np.vstack(
(x_test, tree.array(branch_rec[i], entrystop=options.maxevents)))
x_train = x_train.T
x_test = x_test.T
x_test = array2D_float(x_test)
#Different type of reconstruction variables
#BN normalization
gamma = 0
beta = 0.2
ar = np.array(x_train)
a = K.constant(ar[:, 0])
mean = K.mean(a)
var = K.var(a)
x_train = K.eval(K.batch_normalization(a, mean, var, gamma, beta))
for i in range(1, nvariable):
a = K.constant(ar[:, i])
mean = K.mean(a)
var = K.var(a)
a = K.eval(K.batch_normalization(a, mean, var, gamma, beta))
x_train = np.vstack((x_train, a))
x_train = x_train.T
ar = np.array(x_test)
a = K.constant(ar[:, 0])
mean = K.mean(a)
var = K.var(a)
x_test = K.eval(K.batch_normalization(a, mean, var, gamma, beta))
for i in range(1, nvariable):
a = K.constant(ar[:, i])
mean = K.mean(a)
var = K.var(a)
a = K.eval(K.batch_normalization(a, mean, var, gamma, beta))
x_test = np.vstack((x_test, a))
x_test = x_test.T
#Add noise, remain to be improved
noise = np.random.normal(loc=0.0, scale=0.01, size=x_train.shape)
x_train_noisy = x_train + noise
noise = np.random.normal(loc=0.0, scale=0.01, size=x_test.shape)
x_test_noisy = x_test + noise
x_train = np.clip(x_train, -1., 1.)
x_test = np.clip(x_test, -1., 1.)
x_train_noisy = np.clip(x_train_noisy, -1., 1.)
x_test_noisy = np.clip(x_test_noisy, -1., 1.)
# Network parameters
input_shape = (x_train.shape[1], )
batch_size = 128
latent_dim = 2
# Build the Autoencoder Model
# First build the Encoder Model
inputs = Input(shape=input_shape, name='encoder_input')
x = inputs
# Shape info needed to build Decoder Model
shape = K.int_shape(x)
# Generate the latent vector
latent = Dense(latent_dim, name='latent_vector')(x)
# Instantiate Encoder Model
encoder = Model(inputs, latent, name='encoder')
encoder.summary()
# Build the Decoder Model
latent_inputs = Input(shape=(latent_dim, ), name='decoder_input')
x = Dense(shape[1])(latent_inputs)
x = Reshape((shape[1], ))(x)
outputs = Activation('tanh', name='decoder_output')(x)
# Instantiate Decoder Model
decoder = Model(latent_inputs, outputs, name='decoder')
decoder.summary()
# Autoencoder = Encoder + Decoder
# Instantiate Autoencoder Model
autoencoder = Model(inputs, decoder(encoder(inputs)), name='autoencoder')
autoencoder.summary()
autoencoder.compile(loss='mse', optimizer='adam')
# Train the autoencoder
autoencoder.fit(x_train_noisy,
x_train,
validation_data=(x_test_noisy, x_test),
epochs=options.epochs,
batch_size=batch_size)
# Predict the Autoencoder output from corrupted test imformation
x_decoded = autoencoder.predict(x_test_noisy)
# Draw Comparision Plots
c = TCanvas("c", "c", 700, 700)
fPads1 = TPad("pad1", "Run2", 0.0, 0.29, 1.00, 1.00)
fPads2 = TPad("pad2", "", 0.00, 0.00, 1.00, 0.29)
fPads1.SetBottomMargin(0.007)
fPads1.SetLeftMargin(0.10)
fPads1.SetRightMargin(0.03)
fPads2.SetLeftMargin(0.10)
fPads2.SetRightMargin(0.03)
fPads2.SetBottomMargin(0.25)
fPads1.Draw()
fPads2.Draw()
fPads1.cd()
nbin = 50
min = -1.
max = 1.
variable = "P^{B}"
lbin = (max - min) / nbin
lbin = str(float((max - min) / nbin))
xtitle = branch_rec[options.branch - 1]
ytitle = "Events/" + lbin + "GeV"
h_rec = TH1D("h_rec", "" + ";%s;%s" % (xtitle, ytitle), nbin, min, max)
h_rec.Sumw2()
h_pre = TH1D("h_pre", "" + ";%s;%s" % (xtitle, ytitle), nbin, min, max)
h_pre.Sumw2()
for i in range(x_test_noisy.shape[0]):
h_rec.Fill(x_test_noisy[i][options.branch - 1])
h_pre.Fill(x_decoded[i][options.branch - 1])
h_rec = UnderOverFlow1D(h_rec)
h_pre = UnderOverFlow1D(h_pre)
maxY = TMath.Max(h_rec.GetMaximum(), h_pre.GetMaximum())
h_rec.SetLineColor(2)
h_rec.SetFillStyle(0)
h_rec.SetLineWidth(2)
h_rec.SetLineStyle(1)
h_pre.SetLineColor(3)
h_pre.SetFillStyle(0)
h_pre.SetLineWidth(2)
h_pre.SetLineStyle(1)
h_rec.SetStats(0)
h_pre.SetStats(0)
h_rec.GetYaxis().SetRangeUser(0, maxY * 1.1)
h_rec.Draw("HIST")
h_pre.Draw("same HIST")
h_rec.GetYaxis().SetTitleSize(0.06)
h_rec.GetYaxis().SetTitleOffset(0.78)
theLeg = TLegend(0.5, 0.45, 0.95, 0.82, "", "NDC")
theLeg.SetName("theLegend")
theLeg.SetBorderSize(0)
theLeg.SetLineColor(0)
theLeg.SetFillColor(0)
theLeg.SetFillStyle(0)
theLeg.SetLineWidth(0)
theLeg.SetLineStyle(0)
theLeg.SetTextFont(42)
theLeg.SetTextSize(.05)
theLeg.AddEntry(h_rec, "Reconstruction", "L")
theLeg.AddEntry(h_pre, "Prediction", "L")
theLeg.SetY1NDC(0.9 - 0.05 * 6 - 0.005)
theLeg.SetY1(theLeg.GetY1NDC())
fPads1.cd()
theLeg.Draw()
title = TLatex(
0.91, 0.93, "AE prediction compare with reconstruction, epochs=" +
str(options.epochs))
title.SetNDC()
title.SetTextSize(0.05)
title.SetTextFont(42)
title.SetTextAlign(31)
title.SetLineWidth(2)
title.Draw()
fPads2.cd()
h_Ratio = h_pre.Clone("h_Ratio")
h_Ratio.Divide(h_rec)
h_Ratio.SetLineColor(1)
h_Ratio.SetLineWidth(2)
h_Ratio.SetMarkerStyle(8)
h_Ratio.SetMarkerSize(0.7)
h_Ratio.GetYaxis().SetRangeUser(0, 2)
h_Ratio.GetYaxis().SetNdivisions(504, 0)
h_Ratio.GetYaxis().SetTitle("Pre/Rec")
h_Ratio.GetYaxis().SetTitleOffset(0.35)
h_Ratio.GetYaxis().SetTitleSize(0.13)
h_Ratio.GetYaxis().SetTitleSize(0.13)
h_Ratio.GetYaxis().SetLabelSize(0.11)
h_Ratio.GetXaxis().SetLabelSize(0.1)
h_Ratio.GetXaxis().SetTitleOffset(0.8)
h_Ratio.GetXaxis().SetTitleSize(0.14)
h_Ratio.SetStats(0)
axis1 = TGaxis(min, 1, max, 1, 0, 0, 0, "L")
axis1.SetLineColor(1)
axis1.SetLineWidth(1)
for i in range(1, h_Ratio.GetNbinsX() + 1, 1):
D = h_rec.GetBinContent(i)
eD = h_rec.GetBinError(i)
if D == 0: eD = 0.92
B = h_pre.GetBinContent(i)
eB = h_pre.GetBinError(i)
if B < 0.1 and eB >= B:
eB = 0.92
Err = 0.
if B != 0.:
Err = TMath.Sqrt((eD * eD) / (B * B) + (D * D * eB * eB) /
(B * B * B * B))
h_Ratio.SetBinContent(i, D / B)
h_Ratio.SetBinError(i, Err)
if B == 0.:
Err = TMath.Sqrt((eD * eD) / (eB * eB) + (D * D * eB * eB) /
(eB * eB * eB * eB))
h_Ratio.SetBinContent(i, D / 0.92)
h_Ratio.SetBinError(i, Err)
if D == 0 and B == 0:
h_Ratio.SetBinContent(i, -1)
h_Ratio.SetBinError(i, 0)
h_Ratio.Draw("e0")
axis1.Draw()
c.SaveAs(branch_rec[options.branch - 1] + "_comparision.png")
rms = hRawYieldDistr[-1].GetRMS() / hRawYieldDistr[-1].GetMean() * 100
shift = TMath.Abs(
hRawYields.GetBinContent(iPt) -
hRawYieldDistr[-1].GetMean()) / hRawYieldDistr[-1].GetMean() * 100
syst = TMath.Sqrt(rms**2 + shift**2)
hRMS.SetBinContent(iPt, rms)
hMeanShift.SetBinContent(iPt, shift)
hSyst.SetBinContent(iPt, syst)
cRMS = TCanvas('cRMS', '', 800, 800)
cRMS.DrawFrame(hRMS.GetBinLowEdge(1), 0.1, ptMax, 20.,
';#it{p}_{T} (GeV/#it{c});RMS (%)')
hRMS.DrawCopy('same')
axisSoverB = TGaxis(gPad.GetUxmax(), gPad.GetUymin(), gPad.GetUxmax(),
gPad.GetUymax(), 0.01, 20., 510, "+LG")
axisSoverB.SetLineColor(kRed + 1)
axisSoverB.SetLabelColor(kRed + 1)
axisSoverB.SetLabelFont(42)
axisSoverB.SetLabelSize(0.045)
axisSoverB.SetTitle('S/B (3#sigma)')
axisSoverB.SetTitleOffset(1.2)
axisSoverB.SetLabelOffset(0.012)
axisSoverB.SetTitleColor(kRed + 1)
axisSoverB.SetTitleFont(42)
axisSoverB.SetTitleSize(0.05)
axisSoverB.SetMaxDigits(3)
axisSoverB.Draw()
hSoverB.DrawCopy('same')
cRMS.Update()
cSyst = TCanvas('cSyst', '', 800, 800)
def AddShadedProfile(can, hist):
tobject_collector = []
if can.GetPrimitive('pad_top'):
GetTopPad(can).SetRightMargin(0.08)
GetBotPad(can).SetRightMargin(0.08)
tobject_collector.extend(
AddShadedProfile(can.GetPrimitive('pad_top'), hist))
GetBotPad(can).Modified()
GetBotPad(can).Update()
return tobject_collector
from ROOT import TH1, TGraph, THStack, TColor, kGray, kBlue, TGaxis, TText
listOfPlottedObjects = [
o for o in can.GetListOfPrimitives() if isinstance(o, (TH1, THStack))
]
listOfPlottedObjects += [
o.GetHistogram() for o in can.GetListOfPrimitives()
if isinstance(o, TGraph)
]
#print listOfPlottedObjects
minValue = 0
maxValue = 1
if listOfPlottedObjects:
#maxValue = max([o.GetBinContent(o.GetMaximumBin()) for o in listOfPlottedObjects])
#minValue = min([o.GetBinContent(o.GetMinimumBin()) for o in listOfPlottedObjects])
maxValue = max([
o.GetBinContent(o.GetMaximumBin()) +
o.GetBinError(o.GetMaximumBin()) for o in listOfPlottedObjects
])
minValue = min([
o.GetBinContent(o.GetMinimumBin()) -
o.GetBinError(o.GetMinimumBin()) for o in listOfPlottedObjects
])
# #print [o.GetMaximum() for o in listOfPlottedObjects]
#maxValue = can.GetUymax()
#minValue = can.GetUymin()
#print maxValue, minValue
temp = hist.Clone()
temp.SetName("ShadedProfile")
temp.SetFillStyle(1001)
lightgray = 1001
#color = TColor(lightgray, 0.956, 0.956, 0.956)
temp.SetFillColorAlpha(kGray, 0.15)
temp.SetLineColor(kGray)
#temp.Scale(1./16000)
temp.SetStats(0)
origMax = temp.GetMaximum()
if origMax > 0:
temp.Scale(1. / origMax)
minCanValue = can.GetUymin()
temp.Scale(maxValue - minCanValue)
# Get new min value
for x in range(temp.GetNbinsX() + 1):
temp.AddBinContent(x, minCanValue)
#temp.Scale(0.9*maxValue)
#print maxValue
#for (int x=1; x<=temp->GetXaxis()->GetNbins(); x++)
# temp->SetBinContent(x,temp->GetBinContent(x)+0.61)
can.cd()
temp.Draw("hist same")
tobject_collector.append(temp)
axis = TGaxis(can.GetUxmax(), can.GetUymin(), can.GetUxmax(), maxValue,
0, origMax, 510, "+L")
axis.SetLineColor(kGray)
axis.SetLabelColor(kGray)
#axis.SetTitle("count")
axis.Draw()
tobject_collector.append(axis)
can.SetTicks(can.GetTickx(), 0)
can.Modified()
can.Update()
#text = TText(0,0, "count");
#text.SetTextAlign(13)
#text.SetTextAngle(90)
#text.SetTextColor(kGray)
#text.Draw()
#tobject_collector.append(text)
return tobject_collector
class Differential1D:
""" Base class for displaying 1D differential plots.
Produces a plot comparing the baseline geometry (base) to the geometry specified by
the geom arguement. The quantity plotted is given by the "stat" option, and is
limited to the different statistics created by the StarBASE application. At present,
these include:
stat="radlen" :: plots number of radiation lengths of material encountered
... need to add more stats
The plot will show a solid histogram for the baseline geometry, with the comparison
geometry shown with red hashes. The fractional difference (baseline-comp)/baseline
is shown at the bottom in blue, scaled according to the alternate axis at the right.
base: selects the baseline geometry. [Mandatory]
geom: selects the comparison geometry. [Mandatory]
volume: selects the volume to be compared. [Mandatory]
geomvolume: selects the volume in the comparison geometry. [Default: same]
stat: selects the statistic to compare. [Default: radlen]
xmin, xmax: x-axis range. [Optional]
ymin, ymax: y-axis range. [Optional]
"""
def __init__(self,
base, geom,
volume="EMSS", geomvolume="same",
stat="radlen",
xmin=+0., xmax=-1.,
ymin=+0., ymax=-1.,
canvas=0,
legend=False
):
self.name = volume
self.base = base
self.geom = geom
baseFile = get_geom_file(base) # get the files
compFile = get_geom_file(geom)
hname = "h_"+stat+"_"+volume+"_eta" # retrive the histograms from the files
print "Get histo: " + str(hname)
self.histo_base = stat_histo( stat, volume, file=baseFile )
if ( geomvolume == "same" ):
self.histo_comp = stat_histo( stat, volume, compFile )
else:
self.histo_comp = stat_histo( stat, geomvolume, compFile )
self.histo_diff = self.histo_base.Clone( hname + "_diff" ) # difference the histograms
self.histo_diff.Add( self.histo_comp, -1.0 )
self.histo_diff.Divide(self.histo_base) # diff will be (old-new)/old * 100%
if ( canvas == 0 ):
canvas = TCanvas("temp"+base+geom+volume+geomvolume,"Differential 1D: baseline="+base+" compare="+geom,500,400);
self.canvas = canvas
# Detect and apply optional x-axis range
if ( xmax > xmin ):
self.histo_base.GetXaxis().SetRangeUser(xmin,xmax)
self.histo_comp.GetXaxis().SetRangeUser(xmin,xmax)
# ... or zoom in on an appropriate scale
else:
# auto_range( self.histo_base )
# auto_range( self.histo_comp )
xmin = auto_min( self.histo_base )
xmax = auto_max( self.histo_base )
self.histo_base.GetXaxis().SetRangeUser(xmin,xmax)
self.histo_comp.GetXaxis().SetRangeUser(xmin,xmax)
# xmin = TMath.Min( self.histo_base.GetXaxis().GetXmin(),self.histo_comp.GetXaxis().GetXmin() )
# xmax = TMath.Max( self.histo_base.GetXaxis().GetXmax(),self.histo_comp.GetXaxis().GetXmax() )
# print "xmin="+str(xmin)
# print "xmax="+str(xmax)
# Detect and apply optional y-axis range
if ( ymax > ymin ):
self.histo_base.GetYaxis().SetRangeUser(ymin,ymax)
else:
ymin = self.histo_base.GetMinimum()
ymax = TMath.Max( self.histo_base.GetMaximum(),
self.histo_comp.GetMaximum())
ymax *= 1.05
# Current range in y-axis extends from 0 to ymax. We want
# to expand this to go from -0.2*ymax to ymax.
ymin = -0.2 * ymax
self.histo_base.GetYaxis().SetRangeUser(ymin,ymax)
# Draw the baseline and comparison histograms
self.histo_base.SetLineWidth(2)
self.histo_base.SetFillStyle(1001)
self.histo_base.SetFillColor(22)
self.histo_base.Draw()
self.histo_comp.SetLineColor(2)
self.histo_comp.SetLineStyle(2)
self.histo_comp.SetFillStyle(3345)
self.histo_comp.SetFillColor(2)
self.histo_comp.Draw("same")
# Rescale difference histogram so that it is in percent
self.histo_diff.Scale(100.0)
# This is the maximum of the histogram.
yfull_max = self.histo_diff.GetMaximum()
yfull_min = self.histo_diff.GetMinimum()
yfull = TMath.Max( yfull_max, TMath.Abs( yfull_min ) )
self.max_differential = yfull
yfull *= 1.3
if ( yfull == 0. ):
yfull = 1.0
# We need to rescale the histogram so that it fits w/in 10% of ymax
self.histo_diff.Scale( 0.10 * ymax / yfull )
# Next we shift the histogram down by 0.1 * ymax
nbinx=self.histo_diff.GetNbinsX();
i=1
while ( i<=nbinx ):
self.histo_diff[i] -= 0.1 * ymax
i+=1
# Reset the line color and draw on the same plot
self.histo_diff.SetLineColor(4)
self.histo_diff.Draw("same")
self.line = TLine(xmin, -0.1 * ymax, xmax, -0.1*ymax )
self.line.SetLineStyle(3)
self.line.Draw()
# And superimpose an axis on the new plot
xa = xmax
self.axis = TGaxis( xa, -0.2*ymax, xa, 0.0, -yfull, +yfull, 50510, "-+L" )
self.axis.SetLabelSize(0.03)
self.axis.SetLabelOffset(-0.02)
self.axis.SetLineColor(4)
self.axis.SetTextColor(4)
self.axis.SetLabelColor(4)
self.axis.SetNdivisions(4)
# self.axis.SetTitle("(base-comp)/base [%]")
self.axis.SetTitleSize(0.0175)
self.axis.SetTitleOffset(-0.5)
self.axis.Draw();
# Add the legend if requested
if ( legend ):
self.legend = TLegend( 0.78, 0.80, 0.98, 0.98 )
self.legend.AddEntry( self.histo_base, base )
self.legend.AddEntry( self.histo_comp, geom )
self.legend.AddEntry( self.histo_diff, "#frac{"+base+"-"+geom+"}{"+base+"} [%]" )
self.legend.Draw()
ymin = hxE.GetYaxis().GetXmin()
ymax = hxE.GetYaxis().GetXmax()
dy = (ymax - ymin) / 0.8 #10 margins top and bottom
xmin = hxE.GetXaxis().GetXmin()
xmax = hxE.GetXaxis().GetXmax()
dx = (xmax - xmin) / 0.8
#10 per cent margins left and right
pad2.Range(xmin - 0.1 * dx, ymin - 0.1 * dy, xmax + 0.1 * dx, ymax + 0.1 * dy)
pad2.Draw()
pad2.cd()
hxE.SetMarkerColor(ROOT.kViolet)
hxE.Draw("][scat same")
pad2.Update()
# draw axis on the right side of the pad
axis = TGaxis(xmax, ymin, xmax, ymax, ymin, ymax, 50510, "+L")
axis.SetLineColor(ROOT.kViolet)
axis.SetLabelColor(ROOT.kViolet)
axis.SetTitle("E [GeV]")
axis.SetTitleColor(ROOT.kViolet)
axis.SetLabelSize(0.035)
axis.SetTitleSize(0.035)
axis.Draw()
#### yz
cnv.cd(2)
ROOT.gPad.SetTicks(1, 1)
ROOT.gPad.SetLogz()
ROOT.gPad.SetGridy()
ROOT.gPad.SetGridx()
# hyz = tfile.Get("h2_z_vs_y")
hyz.SetTitle(ptitle + " for #it{B} = " + btitle)
hyz.Draw("col")
