def load_cck():
#model by Guillermo at. al.
f = open("data/data-dtdy-y_0-RHIC-clean_CCK.dat", "read")
sigma = []
for line in f:
if line[0] == "#": continue
p = line.split("\t")
t = float(p[3])
nucl = float(p[4])
hs = float(p[8])
sigma.append({"t": t, "nucl": nucl, "hs": hs})
#correction from gamma-Au to AuAu by Michal
k_auau = 9.0296
#scaling to XnXn
kx = 0.1702
gCCK = TGraphErrors(len(sigma))
for i in xrange(len(sigma)):
gCCK.SetPoint(i, sigma[i]["t"], sigma[i]["hs"] * k_auau * kx)
gCCK.SetLineColor(rt.kRed)
gCCK.SetLineWidth(3)
gCCK.SetLineStyle(rt.kDashed) # 9
return gCCK
def make_tgrapherrors(name,
title,
color=1,
marker=20,
marker_size=1,
width=1,
asym_err=False,
style=1,
x=None,
y=None):
if (x and y) is None:
gr = TGraphErrors() if not asym_err else TGraphAsymmErrors()
else:
gr = TGraphErrors(len(x), array(x, 'd'), array(
y, 'd')) if not asym_err else TGraphAsymmErrors(
len(x), array(x, 'd'), array(y), 'd')
gr.SetTitle(title)
gr.SetName(name)
gr.SetMarkerStyle(marker)
gr.SetMarkerColor(color)
gr.SetLineColor(color)
gr.SetMarkerSize(marker_size)
gr.SetLineWidth(width)
gr.SetLineStyle(style)
return gr
def CompareToys(MwValuemT, MwValuemTStat):
print(len(MwValuemT), len(MwValuemTStat))
n = len(MwValuemT)
x, y = array('d'), array('d')
ex, ey = array('d'), array('d')
for i in range(0, n):
x.append(i + 1)
ex.append(0)
y.append(MwValuemT[i])
ey.append(MwValuemTStat[i])
gr = TGraphErrors(n, x, y, ex, ey)
gr.Draw("P")
gr.SetLineWidth(0)
gr.SetMarkerStyle(20)
gr.SetMarkerSize(1)
xax = gr.GetXaxis()
for i in range(0, n):
binIndex = xax.FindBin(i)
xax.SetBinLabel(binIndex, "toys")
Output = ROOT.TFile.Open("Matrix.root", "RECREATE")
gr.Write("gr")
def getGraph(data, name='graph', offset=0):
from ROOT import TGraphErrors
g = TGraphErrors(len(data))
for n, (_, throughput, throughputE) in enumerate(data):
g.SetPoint(n, offset + n, throughput)
g.SetPointError(n, 0., throughputE)
g.SetName(name)
g.SetLineWidth(2)
g.SetMarkerSize(1.7)
return g
def plotGraph(self, x='vv', y='acc'):
'''
Plot a graph with specified quantities on x and y axes , and saves the graph
'''
if (x not in self.quantities.keys()) or (y not in self.quantities.keys()):
raise RuntimeError('selected quantities not available, available quantities are: \n{}'.format(self.quantities.keys()))
xq = self.quantities[x]
yq = self.quantities[y]
#graph = TGraphAsymmErrors()
#graph = TGraph()
graph = TGraphErrors()
for i,s in enumerate(self.samples):
graph.SetPoint(i,getattr(s, xq.name), getattr(s, yq.name) )
#if xq.err:
# graph.SetPointEXhigh(i, getattr(s, xq.name+'_errup')) # errup errdn
# graph.SetPointEXlow (i, getattr(s, xq.name+'_errdn'))
if yq.err:
graph.SetPointError(i, 0, getattr(s, yq.name+'_errup'))
# graph.SetPointEYhigh(i, getattr(s, yq.name+'_errup'))
# graph.SetPointEYlow (i, getattr(s, yq.name+'_errdn'))
c = TCanvas()
graph.SetLineWidth(2)
graph.SetMarkerStyle(22)
graph.SetTitle(';{x};{y}'.format(y=yq.title,x=xq.title))
graph.Draw('APLE')
if yq.forceRange:
graph.SetMinimum(yq.Range[0])
graph.SetMaximum(yq.Range[1])
gPad.Modified()
gPad.Update()
if yq.name=='expNevts':
line = TLine(gPad.GetUxmin(),3,gPad.GetUxmax(),3)
line.SetLineColor(ROOT.kBlue)
line.Draw('same')
#graph.SetMinimum(0.01)
#graph.SetMaximum(1E06)
if xq.log: c.SetLogx()
if yq.log: c.SetLogy()
c.SetGridx()
c.SetGridy()
c.SaveAs('./plots/{}{}/{}_{}VS{}.pdf'.format(self.label,suffix,self.name,yq.name,xq.name))
c.SaveAs('./plots/{}{}/{}_{}VS{}.C'.format(self.label,suffix,self.name,yq.name,xq.name))
c.SaveAs('./plots/{}{}/{}_{}VS{}.png'.format(self.label,suffix,self.name,yq.name,xq.name))
self.graphs['{}VS{}'.format(yq.name,xq.name)] = graph
# add the graph container for memory?
graph_saver.append(graph)
def draw_limits_per_category(nchannels, xmin, xmax, obs, expect, upper1sig,
lower1sig, upper2sig, lower2sig):
channel = np.array(
[3. * nchannels - 1.5 - 3. * i for i in range(0, nchannels)])
ey = np.array([0.8 for i in range(0, nchannels)])
zero = np.zeros(nchannels)
gexpect1sig = TGraphAsymmErrors(nchannels, expect, channel, lower1sig,
upper1sig, ey, ey)
gexpect1sig.SetFillColor(kGreen)
gexpect1sig.SetLineWidth(2)
gexpect1sig.SetLineStyle(2)
gexpect2sig = TGraphAsymmErrors(nchannels, expect, channel, lower2sig,
upper2sig, ey, ey)
gexpect2sig.SetFillColor(kYellow)
gexpect2sig.SetLineWidth(2)
gexpect2sig.SetLineStyle(2)
gexpect2sig.Draw("2")
gexpect1sig.Draw("2")
gobs = TGraphErrors(nchannels, obs, channel, zero, ey)
gobs.SetMarkerStyle(21)
gobs.SetMarkerSize(1.5)
gobs.SetLineWidth(2)
gobs.Draw("pz")
# dashed line at median expected limits
l = TLine()
l.SetLineStyle(2)
l.SetLineWidth(2)
for bin in range(nchannels):
l.DrawLine(expect[bin], channel[bin] - ey[bin], expect[bin],
channel[bin] + ey[bin])
# line to separate individual and combined limits
l.SetLineStyle(1)
l.SetLineWidth(1)
l.DrawLine(xmin, 0, xmax, 0)
# legend
x1 = gStyle.GetPadLeftMargin() + 0.01
y2 = 1 - gStyle.GetPadTopMargin() - 0.01
leg = TLegend(x1, y2 - 0.17, x1 + 0.25, y2)
leg.SetFillColor(4000)
leg.AddEntry(gexpect1sig, "Expected #pm1#sigma", "FL")
leg.AddEntry(gexpect2sig, "Expected #pm2#sigma", "FL")
leg.AddEntry(gobs, "Observed", "pl")
leg.Draw()
return gobs, gexpect1sig, gexpect2sig, leg
def draw_limits_per_category(nchannels, xmin, xmax, obs, expect, upper1sig,
lower1sig, upper2sig, lower2sig):
channel = np.array(
[nchannels - 1.5 - float(i) for i in range(0, nchannels)])
ey = np.full(nchannels, 0.494)
zero = np.zeros(nchannels)
gexpect1sig = TGraphAsymmErrors(nchannels, expect, channel, lower1sig,
upper1sig, ey, ey)
gexpect1sig.SetFillColor(kGreen)
gexpect1sig.SetLineWidth(2)
gexpect1sig.SetLineStyle(2)
gexpect2sig = TGraphAsymmErrors(nchannels, expect, channel, lower2sig,
upper2sig, ey, ey)
gexpect2sig.SetFillColor(kYellow)
gexpect2sig.SetLineWidth(2)
gexpect2sig.SetLineStyle(2)
gexpect2sig.Draw("2")
gexpect1sig.Draw("2")
gobs = TGraphErrors(nchannels, obs, channel, zero, ey)
gobs.SetMarkerStyle(21)
gobs.SetMarkerSize(1.5)
gobs.SetLineWidth(2)
#gobs.Draw("pz")
# dashed line at median expected limits
l = TLine()
l.SetLineStyle(2)
l.SetLineWidth(2)
for bin in range(nchannels):
l.DrawLine(expect[bin], channel[bin] - ey[bin], expect[bin],
channel[bin] + ey[bin])
# line to separate individual and combined limits
l.SetLineStyle(1)
l.SetLineWidth(1)
l.DrawLine(xmin, 0, xmax, 0)
# legend
leg = TLegend(0.75, 0.75, 0.95, 0.9)
leg.SetFillColor(4000)
leg.AddEntry(gexpect1sig, "Expected #pm1#sigma", "FL")
leg.AddEntry(gexpect2sig, "Expected #pm2#sigma", "FL")
#leg.AddEntry( gobs, "Observed", "pl" )
leg.Draw()
return gobs, gexpect1sig, gexpect2sig, leg
def Plot2DHist( th2, slope, offset, x_points, y_points, error_points, outname, xlabel='',
etBinIdx=None, etaBinIdx=None, etBins=None,etaBins=None):
from ROOT import TCanvas, gStyle, TLegend, kRed, kBlue, kBlack,TLine,kBird, kOrange,kGray
from ROOT import TGraphErrors,TF1,TColor
import array
def AddTopLabels(can, etlist = None, etalist = None, etidx = None, etaidx = None, logger=None):
extraText = [GetAtlasInternalText()]
if etlist and etidx is not None:
etlist=copy(etlist)
if etlist[-1]>9999: etlist[-1]='#infty'
binEt = (str(etlist[etidx]) + ' < E_{T} [GeV] < ' + str(etlist[etidx+1]) if etidx+1 < len(etlist) else
'E_{T} > ' + str(etlist[etidx]) + ' GeV')
extraText.append(binEt)
if etalist and etaidx is not None:
binEta = (str(etalist[etaidx]) + ' < #eta < ' + str(etalist[etaidx+1]) if etaidx+1 < len(etalist) else
str(etalist[etaidx]) + ' < #eta < 2.47')
extraText.append(binEta)
DrawText(can,extraText,.14,.68,.35,.93,totalentries=4)
gStyle.SetPalette(kBird)
drawopt='lpE2'
canvas = TCanvas('canvas','canvas',500, 500)
canvas.SetRightMargin(0.15)
th2.GetXaxis().SetTitle('Neural Network output (Discriminant)')
th2.GetYaxis().SetTitle(xlabel)
th2.GetZaxis().SetTitle('Count')
th2.Draw('colz')
canvas.SetLogz()
g = TGraphErrors(len(x_points), array.array('d',x_points), array.array('d',y_points), array.array('d',error_points), array.array('d',[0]*len(x_points)))
g.SetLineWidth(1)
g.SetLineColor(kBlue)
g.SetMarkerColor(kBlue)
g.Draw("P same")
line = TLine(slope*th2.GetYaxis().GetXmin()+offset,th2.GetYaxis().GetXmin(), slope*th2.GetYaxis().GetXmax()+offset, th2.GetYaxis().GetXmax())
line.SetLineColor(kBlack)
line.SetLineWidth(2)
line.Draw()
AddTopLabels( canvas, etlist=etBins,etalist=etaBins,etidx=etBinIdx,etaidx=etaBinIdx)
FormatCanvasAxes(canvas, XLabelSize=16, YLabelSize=16, XTitleOffset=0.87, ZLabelSize=14,ZTitleSize=14, YTitleOffset=0.87, ZTitleOffset=1.1)
SetAxisLabels(canvas,'Neural Network output (Discriminant)',xlabel)
canvas.SaveAs(outname+'.pdf')
canvas.SaveAs(outname+'.C')
return outname+'.pdf'
def getGraph(self, filename): data = self.getData(filename) from ROOT import TFile, TTree, gDirectory, TGraphErrors, TCanvas from array import array nsteps = len(data) g = TGraphErrors(nsteps) ## Loop on the data for n,(fragsize,tp,tpE) in enumerate(data): g.SetPoint( n, fragsize, tp) g.SetPointError( n, 0., tpE) g.SetLineWidth(2) g.SetMarkerSize(1.7) return g
def load_ms():
#model by Heikki and Bjorn
f = open("data/to_star_ms.txt", "read")
t_sigma = []
for line in f:
if line[0] == "#": continue
point = line.split(" ")
t_sigma.append([float(point[0]), float(point[1])])
#scaling to XnXn
kx = 0.1702
gMS = TGraphErrors(len(t_sigma))
for i in xrange(len(t_sigma)):
gMS.SetPoint(i, t_sigma[i][0], t_sigma[i][1] * kx)
gMS.SetLineColor(rt.kViolet)
gMS.SetLineWidth(3)
gMS.SetLineStyle(rt.kDashDotted) # kDashed
return gMS
def load_sartre():
sartre = TFile.Open(
"/home/jaroslav/sim/sartre_tx/sartre_AuAu_200GeV_Jpsi_coh_2p7Mevt.root"
)
sartre_tree = sartre.Get("sartre_tree")
hSartre = ut.prepare_TH1D("hSartre", 0.002, 0., 0.12)
sartre_tree.Draw("-tval >> hSartre", "rapidity>-1 && rapidity<1")
ut.norm_to_integral(hSartre, 0.025) # now same as Starlight
gSartre = TGraphErrors(hSartre.GetNbinsX())
for ibin in xrange(1, hSartre.GetNbinsX() + 1):
gSartre.SetPoint(ibin - 1, hSartre.GetBinCenter(ibin),
hSartre.GetBinContent(ibin))
gSartre.SetLineColor(rt.kYellow + 1)
gSartre.SetLineWidth(3)
#gSartre.SetLineStyle(rt.kDashDotted)
return gSartre
def main(network_name, do_weights, general_weights, electron_weights,
muon_weights, data_files, no_true_comp, nbins, veto_all_jets,
require_jets, WorkingP_List, Independant_Variable_List,
Histogram_Variable_List, Profile_List, TH2D_List):
TH1.SetDefaultSumw2()
## First we check that you havent vetoed and requested jets
if veto_all_jets and require_jets:
print("\n\n\nYOU CANT ASK FOR NONE YET SOME JETS\n\n\n")
return 0
for data_dir, data_set_name in data_files:
OUTPUT_dir = os.path.join(os.environ["HOME_DIRECTORY"], "Output")
DATA_dir = os.path.join(os.environ["HOME_DIRECTORY"], "Data")
data_base_dir = os.path.join(DATA_dir, data_dir)
print("\n\n" + data_set_name)
"""
____ ____ _____ ____ _ ____ _ _____ ___ ___ _ _
| _ \ | _ \ | ____| | _ \ / \ | _ \ / \ |_ _| |_ _| / _ \ | \ | |
| |_) | | |_) | | _| | |_) | / _ \ | |_) | / _ \ | | | | | | | | | \| |
| __/ | _ < | |___ | __/ / ___ \ | _ < / ___ \ | | | | | |_| | | |\ |
|_| |_| \_\ |_____| |_| /_/ \_\ |_| \_\ /_/ \_\ |_| |___| \___/ |_| \_|
"""
## The list of matrices
Indp_output = [None for var in Independant_Variable_List]
TH1D_output = [[None for wp in WorkingP_List]
for var in Histogram_Variable_List]
Tail_output = [[None for wp in WorkingP_List]
for var in Histogram_Variable_List]
Prof_output = [[None for wp in WorkingP_List] for var in Profile_List]
TH2D_output = [[None for wp in WorkingP_List] for var in TH2D_List]
## Open the root file
root_file = TFile.Open(os.path.join(data_base_dir, data_set_name))
## Loading the tree containing the working point information
wpt_tree = root_file.Get("wpt_tree")
## Making the wpt_tree and the other trees friends so that they can be compared
wpt_tree.AddFriend(network_name, root_file)
wpt_tree.AddFriend("alt_tree", root_file)
wpt_tree.AddFriend("var_tree", root_file)
## Creating a string of all the weights to be applied
event_weights = []
if "SR" in data_dir: event_weights += general_weights
if "ee" in data_dir: event_weights += electron_weights
if "mumu" in data_dir: event_weights += muon_weights
if veto_all_jets:
event_weights += ["(var_tree.Jets_Loose_SumET==0)"]
if require_jets:
event_weights += ["(var_tree.Jets_Loose_SumET>0)"]
if do_weights: weight_string = " * ".join(event_weights)
else: weight_string = ""
if len(weight_string):
print("Events are weighted using: {}".format(weight_string))
## The strings to call up the Truth Values
if no_true_comp:
True_Et = "0"
True_Ex = "0"
True_Ey = "0"
True_Phi = "0"
else:
True_Et = "WP_Truth_ET"
True_Ex = "WP_Truth_X"
True_Ey = "WP_Truth_Y"
True_Phi = "WP_Truth_Phi"
"""
____ ____ _ __ __ ___ _ _ ____
| _ \ | _ \ / \ \ \ / / |_ _| | \ | | / ___|
| | | | | |_) | / _ \ \ \ /\ / / | | | \| | | | _
| |_| | | _ < / ___ \ \ V V / | | | |\ | | |_| |
|____/ |_| \_\ /_/ \_\ \_/\_/ |___| |_| \_| \____|
"""
## Before the workingpoint loop, the independant variables are drawn
for v, var in enumerate(Independant_Variable_List):
print(" -- {}".format(var.name))
## Creating the histogram which will be filled
hist_name = var.name
myhist = TH1D(hist_name, hist_name, var.nbins, var.xmin, var.xmax)
myhist.SetStats(True)
myhist.StatOverflows(True)
## Get Maths Function from variable
maths_string = var.tree + "." + var.branch
## Drawing the tree and saving the hist to the matrix
execution = "{}>>{}".format(maths_string, hist_name)
wpt_tree.Draw(execution, weight_string, "goff")
## Saving the Histogram to the Matrix
myhist.SetDirectory(0)
Indp_output[v] = myhist
## First we select the working point and create the correct strings
for w, wp in enumerate(WorkingP_List):
print(" -- {}:".format(wp.name))
Rec_Et = wp.Et
Rec_Ex = wp.Ex
Rec_Ey = wp.Ey
Rec_Phi = wp.Phi
if wp.tree == "ann_tree":
Rec_Et = network_name + "." + Rec_Et
Rec_Ex = network_name + "." + Rec_Ex
Rec_Ey = network_name + "." + Rec_Ey
rec_and_truth_vars = [
Rec_Et, Rec_Ex, Rec_Ey, Rec_Phi, True_Et, True_Ex, True_Ey,
True_Phi
]
## Drawing the 1D histograms
for v, var in enumerate(Histogram_Variable_List):
print(" -- -- {}".format(var.name))
## Creating the histogram which will be filled
hist_name = "{}_{}".format(var.name, wp.name)
myhist = TH1D(hist_name, hist_name, var.nbins, var.xmin,
var.xmax)
myhist.SetStats(True)
myhist.StatOverflows(True)
## Individual special plots
if var.name == "XY":
## Plot the X histogram
maths_string = Evaluation.TH1D_Maths_String(
"X", *rec_and_truth_vars)
execution = "{} >> {}".format(maths_string, hist_name)
wpt_tree.Draw(execution, weight_string, "goff")
## Add the y histogram
maths_string = Evaluation.TH1D_Maths_String(
"Y", *rec_and_truth_vars)
execution = "{} >> +{}".format(maths_string, hist_name)
wpt_tree.Draw(execution, weight_string, "goff")
else:
## Get Maths Function from variable
maths_string = Evaluation.TH1D_Maths_String(
var.name, *rec_and_truth_vars)
## Drawing the tree and saving the hist to the matrix
execution = "{} >> {}".format(maths_string, hist_name)
wpt_tree.Draw(execution, weight_string, "goff")
## Saving the Histogram to the Matrix
myhist.SetDirectory(0)
TH1D_output[v][w] = myhist
## Drawing the Profiles
for v, (vx, vy) in enumerate(Profile_List):
print(" -- -- {} vs {}".format(vx.name, vy.name))
## Creating the profile which will be filled
hist_name = "{}_vs_{}_{}".format(vx.name, vy.name, wp.name)
myhist = TProfile(hist_name, hist_name, vx.nbins, vx.xmin,
vx.xmax)
if vy.reso: myhist.SetErrorOption('s')
## The x variable is called from its branch in the correct tree
x_string = vx.tree + "." + vx.branch
## Individual special plots
if vy.name == "XY":
y_string = Evaluation.TH1D_Maths_String(
"X", *rec_and_truth_vars)
execution = "{}:{} >> {}".format(y_string, x_string,
hist_name)
wpt_tree.Draw(execution, weight_string, "goff prof")
y_string = Evaluation.TH1D_Maths_String(
"Y", *rec_and_truth_vars)
execution = "{}:{} >>+{}".format(y_string, x_string,
hist_name)
wpt_tree.Draw(execution, weight_string, "goff prof")
elif vy.name == "AZ":
z_x = "(alt_tree.ll_px)"
z_y = "(alt_tree.ll_py)"
z_pt = "(alt_tree.ll_pt)"
x_string = z_pt
y_string = "({rx}*{zx} + {ry}*{zy})/{zpt}".format(
rx=Rec_Ex, ry=Rec_Ey, zx=z_x, zy=z_y, zpt=z_pt)
execution = "{}:{} >> {}".format(y_string, x_string,
hist_name)
wpt_tree.Draw(execution, weight_string, "goff prof")
else:
y_string = Evaluation.TH1D_Maths_String(
vy.name, *rec_and_truth_vars)
execution = "{}:{} >> {}".format(y_string, x_string,
hist_name)
wpt_tree.Draw(execution, weight_string, "goff prof")
## Saving the Histogram to the Matrix
myhist.SetDirectory(0)
Prof_output[v][w] = myhist
if wp.name not in ["Network", "Tight"]:
continue
## Drawing the TH2Ds
for v, (vx, vy) in enumerate(TH2D_List):
print(" -- -- {} vs {}".format(vx.name, vy.name))
## Creating the profile which will be filled
hist_name = "2D_{}_vs_{}_{}".format(vx.name, vy.name, wp.name)
myhist = TH2D(hist_name, hist_name, vx.nbins2d, vx.xmin2d,
vx.xmax2d, vy.nbins2d, vy.xmin2d, vy.xmax2d)
x_string = Evaluation.TH1D_Maths_String(
vx.name, *rec_and_truth_vars)
y_string = Evaluation.TH1D_Maths_String(
vy.name, *rec_and_truth_vars)
## The x variable is called from its branch in the correct tree
if x_string is None:
x_string = vx.tree + "." + vx.branch
execution = "{}:{} >> {}".format(y_string, x_string, hist_name)
wpt_tree.Draw(execution, weight_string, "goff")
## Saving the Histogram to the Matrix
myhist.SetDirectory(0)
TH2D_output[v][w] = myhist
root_file.Close()
"""
_____ ____ ___ _____ ___ _ _ ____
| ____| | _ \ |_ _| |_ _| |_ _| | \ | | / ___|
| _| | | | | | | | | | | | \| | | | _
| |___ | |_| | | | | | | | | |\ | | |_| |
|_____| |____/ |___| |_| |___| |_| \_| \____|
"""
## We now go through the 1D histograms and make them include overflow and underflow
for v, var in enumerate(Histogram_Variable_List):
for w, wp in enumerate(WorkingP_List):
## Include the overflow
last_bin = TH1D_output[v][w].GetBinContent(nbins)
overflow = TH1D_output[v][w].GetBinContent(nbins + 1)
TH1D_output[v][w].SetBinContent(nbins, last_bin + overflow)
TH1D_output[v][w].SetBinContent(nbins + 1, 0)
## Include the underflow
first_bin = TH1D_output[v][w].GetBinContent(1)
underflow = TH1D_output[v][w].GetBinContent(0)
TH1D_output[v][w].SetBinContent(1, first_bin + underflow)
TH1D_output[v][w].SetBinContent(0, 0)
## We create a tail distrobution if it is requested
if var.tail:
tail_temp = TH1D_output[v][w].GetCumulative(False)
tail_temp.Scale(1 / tail_temp.GetBinContent(1))
Tail_output[v][w] = tail_temp
## We go through the resolution profiles and replace their entries with the RMSE for each bin
for v, (vx, vy) in enumerate(Profile_List):
if not vy.reso:
continue
for w, wp in enumerate(WorkingP_List):
old_prof = Prof_output[v][w]
bin_width = old_prof.GetBinWidth(1)
name = "{}_vs_{}_{}_res".format(vx.name, vy.name, wp.name)
new_prof = TGraphErrors(vx.nbins)
new_prof.SetName(name)
new_prof.SetTitle(name)
new_prof.SetLineWidth(2)
for b_idx in range(vx.nbins):
new_prof.SetPoint(b_idx, old_prof.GetBinCenter(b_idx + 1),
old_prof.GetBinError(b_idx + 1))
new_prof.SetPointError(b_idx, bin_width / 2, 0)
Prof_output[v][w] = new_prof
"""
____ _ __ __ ___ _ _ ____
/ ___| / \ \ \ / / |_ _| | \ | | / ___|
\___ \ / _ \ \ \ / / | | | \| | | | _
___) | / ___ \ \ V / | | | |\ | | |_| |
|____/ /_/ \_\ \_/ |___| |_| \_| \____|
"""
## Creating the output directory
output_dir = os.path.join(OUTPUT_dir, network_name, data_set_name[:-5])
if veto_all_jets:
output_dir = output_dir + "_NOJETS"
if require_jets:
output_dir = output_dir + "_SOMEJETS"
## Check that the file can be saved
if not os.path.exists(output_dir):
os.system("mkdir -p " + output_dir)
## We create an output file for the histograms
output_file = TFile(os.path.join(output_dir, "histograms.root"),
"update")
gFile = output_file
## We save the independants
for v, var in enumerate(Independant_Variable_List):
Indp_output[v].Write("", TObject.kOverwrite)
## We save the TH1Ds and tails
for v, var in enumerate(Histogram_Variable_List):
for w in range(len(WorkingP_List)):
TH1D_output[v][w].Write("", TObject.kOverwrite)
if var.tail:
Tail_output[v][w].Write("", TObject.kOverwrite)
## We save the profiles
for v in range(len(Profile_List)):
for w in range(len(WorkingP_List)):
Prof_output[v][w].Write("", TObject.kOverwrite)
## We save the TH2Ds
for v in range(len(TH2D_List)):
for w in range(len(WorkingP_List[:2])):
TH2D_output[v][w].Write("", TObject.kOverwrite)
output_file.Close()
return 0
def mistagSFtopEMu(year_, ana_):
if year_ == 2017:
dir = "monohbb.v06.00.05.2017_NCU/withSingleTop/" + ana_ + "/"
if year_ == 2018:
dir = "monohbb.v06.00.05.2018_NCU/withSingleTop/" + ana_ + "/"
print "Top Electron Region"
print " "
openfile1 = TFile(dir + "TopE.root") #
topmatchTopE = openfile1.Get("h_TopMatch")
WmatchTopE = openfile1.Get("h_Wmatch")
unmatchTopE = openfile1.Get("h_unmatch")
wjetsTopE = openfile1.Get("h_sumWJets")
dibosonTopE = openfile1.Get("h_sumDiboson")
unsubtractedDataTopE = openfile1.Get("h_Data")
failMCsubtractTopE = openfile1.Get("h_ttFailed")
passMCsubtractTopE = openfile1.Get("h_ttPassed")
subtractedDataTopE = openfile1.Get("SubtractedData")
datafailTopE = openfile1.Get("SubtractedDataFailed")
datapassTopE = openfile1.Get("SubtractedDataPassed") #
totaldataTopE = openfile1.Get("h_totaldata")
totalMCtopE = openfile1.Get("h_tt")
passedTopEdataBfrSubtract = openfile1.Get("h_Data_Passed")
wjetsTopEpassed = openfile1.Get("h_sumWJetsPassed")
dibosonTopEpassed = openfile1.Get("h_sumDibosonPassed")
failedTopEdataBfrSubtract = openfile1.Get("h_Data_Failed")
wjetsTopEfailed = openfile1.Get("h_sumWJetsFailed")
dibosonTopEfailed = openfile1.Get("h_sumDibosonFailed")
stTopE = openfile1.Get("h_sumST")
stTopEpassed = openfile1.Get("h_sumSTPassed")
stTopEfailed = openfile1.Get("h_sumSTFailed")
print "Top Muon Region"
print " "
openfile2 = TFile(dir + "TopMu.root") #
topmatchTopMu = openfile2.Get("h_TopMatch")
WmatchTopMu = openfile2.Get("h_Wmatch")
unmatchTopMu = openfile2.Get("h_unmatch")
wjetsTopMu = openfile2.Get("h_sumWJets")
dibosonTopMu = openfile2.Get("h_sumDiboson")
unsubtractedDataTopMu = openfile2.Get("h_Data")
failMCsubtractTopMu = openfile2.Get("h_ttFailed")
passMCsubtractTopMu = openfile2.Get("h_ttPassed")
subtractedDataTopMu = openfile2.Get("SubtractedData")
datafailTopMu = openfile2.Get("SubtractedDataFailed")
datapassTopMu = openfile2.Get("SubtractedDataPassed") #
totaldataTopMu = openfile2.Get("h_totaldata")
totalMCtopMu = openfile2.Get("h_tt")
passedTopMudataBfrSubtract = openfile2.Get("h_Data_Passed")
wjetsTopMupassed = openfile2.Get("h_sumWJetsPassed")
dibosonTopMupassed = openfile2.Get("h_sumDibosonPassed")
failedTopMudataBfrSubtract = openfile2.Get("h_Data_Failed")
wjetsTopMufailed = openfile2.Get("h_sumWJetsFailed")
dibosonTopMufailed = openfile2.Get("h_sumDibosonFailed")
stTopMu = openfile2.Get("h_sumST")
stTopMupassed = openfile2.Get("h_sumSTPassed")
stTopMufailed = openfile2.Get("h_sumSTFailed")
print "get histograms from root files: done"
print " "
SubtractedDataPassedTopE = datapassTopE.Clone("SubtractedDataPassedTopE")
SubtractedDataPassedTopMu = datapassTopMu.Clone(
"SubtractedDataPassedTopMu")
#merge histogram"
print "merge histograms"
print " "
topmatchMerge = topmatchTopE + topmatchTopMu
WmatchMerge = WmatchTopE + WmatchTopMu
unmatchMerge = unmatchTopE + unmatchTopMu
wjetsMerge = wjetsTopE + wjetsTopMu
stMerge = stTopE + stTopMu
dibosonMerge = dibosonTopE + dibosonTopMu
unsubtractedDataMerge = unsubtractedDataTopE + unsubtractedDataTopMu
failMCsubtractMerge = failMCsubtractTopE.Clone("failMCsubtractMerge")
failMCsubtractMerge = failMCsubtractMerge + failMCsubtractTopMu
passMCsubtractMerge = passMCsubtractTopE.Clone("passMCsubtractMerge")
passMCsubtractMerge = passMCsubtractMerge + passMCsubtractTopMu
subtractedDataMerge = subtractedDataTopE + subtractedDataTopMu
ttData_fraction = arr.array('d')
ttData_error = arr.array('d')
totaldataMerge = totaldataTopE + totaldataTopMu
totaldata = totaldataMerge.Integral()
totaldataMerge.Rebin(14)
datafailMerge = datafailTopE + datafailTopMu
faildata = datafailMerge.Integral()
datafailMerge.Rebin(14)
datafailMerge.Sumw2()
datafailMerge.Divide(totaldataMerge)
frac_ttData_fail = datafailMerge.Integral()
ttData_fraction.append(frac_ttData_fail)
ttData_error.append(datafailMerge.GetBinError(1))
datapassMerge = datapassTopE + datapassTopMu
passdata = datapassMerge.Integral()
datapassMerge.Rebin(14)
datapassMerge.Sumw2()
datapassMerge.Divide(totaldataMerge)
frac_ttData_pass = datapassMerge.Integral()
ttData_fraction.append(frac_ttData_pass)
ttData_error.append(datapassMerge.GetBinError(1))
ttMC_fraction = arr.array('d')
ttMC_error = arr.array('d')
totalMCmerge = totalMCtopE + totalMCtopMu
totalMCmerge.Rebin(14)
MCfailTopE = failMCsubtractTopE.Clone("MCfailTopE")
MCfailTopMu = failMCsubtractTopMu.Clone("MCfailTopMu")
MCfailMerge = MCfailTopE + MCfailTopMu
MCfailMerge.Rebin(14)
MCfailMerge.Sumw2()
MCfailMerge.Divide(totalMCmerge)
frac_Failed_fin = MCfailMerge.Integral()
ttMC_fraction.append(frac_Failed_fin)
ttMC_error.append(MCfailMerge.GetBinError(1))
MCpassTopE = passMCsubtractTopE.Clone("MCpassTopE")
MCpassTopMu = passMCsubtractTopMu.Clone("MCpassTopMu")
MCpassMerge = MCpassTopE + MCpassTopMu
MCpassMerge.Rebin(14)
MCpassMerge.Sumw2()
MCpassMerge.Divide(totalMCmerge)
frac_Passed_fin = MCpassMerge.Integral()
ttMC_fraction.append(frac_Passed_fin)
ttMC_error.append(MCpassMerge.GetBinError(1))
#print "\nttMC_fraction:", ttMC_fraction
#print "ttMC_error:", ttMC_error
sfMerge = datapassMerge.Clone("sfMerge")
sfMerge.Sumw2()
sfMerge.Divide(MCpassMerge)
stacklist = []
stacklist.append(dibosonMerge)
stacklist.append(stMerge)
stacklist.append(wjetsMerge)
stacklist.append(unmatchMerge)
stacklist.append(WmatchMerge)
stacklist.append(topmatchMerge)
print "merge histograms: done"
print " "
print "draw histograms"
print " "
#----------------------- canvas 1 -----------------------#
c1 = TCanvas("c1", "", 800, 900) #width-height
c1.SetTopMargin(0.4)
c1.SetBottomMargin(0.05)
c1.SetRightMargin(0.1)
c1.SetLeftMargin(0.15)
gStyle.SetOptStat(0)
binvalues1 = []
for i in range(14):
binvalue = unsubtractedDataMerge.GetBinContent(i)
binvalues1.append(binvalue)
totalmax = max(binvalues1) + 100
padMain = TPad("padMain", "", 0.0, 0.25, 1.0, 0.97)
padMain.SetTopMargin(0.4)
padMain.SetRightMargin(0.05)
padMain.SetLeftMargin(0.17)
padMain.SetBottomMargin(0.03)
padMain.SetTopMargin(0.1)
padRatio = TPad("padRatio", "", 0.0, 0.0, 1.0, 0.25)
padRatio.SetRightMargin(0.05)
padRatio.SetLeftMargin(0.17)
padRatio.SetTopMargin(0.05)
padRatio.SetBottomMargin(0.3)
padMain.Draw()
padRatio.Draw()
padMain.cd()
gPad.GetUymax()
leg1 = myLegend(coordinate=[0.45, 0.57, 0.65, 0.6])
unsubtractedDataMerge.SetMaximum(totalmax)
unsubtractedDataMerge.SetLineColor(1)
unsubtractedDataMerge.SetMarkerStyle(20)
unsubtractedDataMerge.SetMarkerSize(1.5)
unsubtractedDataMerge.GetXaxis().SetLabelSize(0)
unsubtractedDataMerge.GetXaxis().SetTitleSize(0)
unsubtractedDataMerge.GetXaxis().SetTitle("DDB")
unsubtractedDataMerge.GetYaxis().SetTitle("Events/Bin")
leg1.AddEntry(unsubtractedDataMerge, "Data", "lep")
unsubtractedDataMerge.Draw("e1")
stackhisto = myStack(colorlist_=[627, 800, 854, 813, 822, 821],
backgroundlist_=stacklist,
legendname_=[
"Diboson", "Single Top", "W+Jets",
"Top (unmtch.)", "Top (W-mtch.)", "Top (mtch.)"
])
stackhisto[0].Draw("histsame")
unsubtractedDataMerge.Draw("e1same")
stackhisto[1].Draw()
leg1.Draw()
lt = TLatex()
lt.DrawLatexNDC(0.23, 0.85,
"#scale[0.8]{CMS} #scale[0.65]{#bf{#it{Internal}}}")
if ana_ == "Inclusive":
lt.DrawLatexNDC(0.17, 0.92, "#scale[0.7]{#bf{" + ana_ + "}}")
if ana_ == "PT-200-350" or ana_ == "PT-350-500" or ana_ == "PT-500-2000":
words = ana_.split("-")
if words[2] == "2000":
lt.DrawLatexNDC(0.17, 0.92,
"#scale[0.7]{#bf{p_{T} " + words[1] + "-Inf GeV}}")
else:
lt.DrawLatexNDC(
0.17, 0.92, "#scale[0.7]{#bf{p_{T} " + words[1] + "-" +
words[2] + " GeV}}")
else:
words = ana_.split("-")
lt.DrawLatexNDC(
0.17, 0.92, "#scale[0.7]{#bf{" + words[0] + " " + words[1] + "-" +
words[2] + " GeV}}")
lt.DrawLatexNDC(0.23, 0.8, "#scale[0.7]{#bf{t#bar{t} CR (e+#mu)}}")
lt.DrawLatexNDC(0.23, 0.75, "#scale[0.5]{#bf{2-prong (bq) enriched}}")
if year_ == 2017:
lt.DrawLatexNDC(0.71, 0.92, "#scale[0.7]{#bf{41.5 fb^{-1} (13 TeV)}}")
if year_ == 2018:
lt.DrawLatexNDC(0.71, 0.92,
"#scale[0.7]{#bf{58.827 fb^{-1} (13 TeV)}}")
padRatio.cd()
gPad.GetUymax()
h_totalBkg = topmatchMerge.Clone("h_totalBkg")
h_totalBkg = h_totalBkg + WmatchMerge + unmatchMerge + wjetsMerge + dibosonMerge
ratio = dataPredRatio(data_=unsubtractedDataMerge, totalBkg_=h_totalBkg)
ratio.SetLineColor(1)
ratio.SetLineWidth(3)
ratio.SetMarkerSize(1.5)
ratio.GetXaxis().SetLabelSize(0.13)
ratio.GetXaxis().SetTitleOffset(1)
ratio.GetXaxis().SetTitleSize(0.13)
ratio.GetXaxis().SetTickLength(0.1)
ratio.GetYaxis().SetLabelSize(0.12)
ratio.GetYaxis().SetTitleOffset(0.5)
ratio.GetYaxis().SetTitleSize(0.13)
ratio.GetYaxis().SetNdivisions(405)
ratio.GetYaxis().SetTitle("#frac{Data-Pred}{Pred}")
ratio.GetXaxis().SetTitle("DDB")
ratio.Draw("e1")
c1.SaveAs(dir + "Merge_all.pdf") #
#----------------------- canvas 2 -----------------------#
c2 = myCanvas(c="c2")
gPad.GetUymax()
leg2 = myLegend()
binvalues2 = []
for i in range(14):
binvalue = subtractedDataMerge.GetBinContent(i)
binvalues2.append(binvalue)
ttmax = max(binvalues2) + 50
failMCsubtractMerge.SetMaximum(ttmax)
failMCsubtractMerge.SetFillColor(821)
failMCsubtractMerge.SetLineColor(821) #922
failMCsubtractMerge.GetXaxis().SetTitle("DDB")
failMCsubtractMerge.GetYaxis().SetTitle("Events/Bin")
leg2.AddEntry(failMCsubtractMerge, "t#bar{t}", "f")
passMCsubtractMerge.SetFillColor(622)
passMCsubtractMerge.SetLineColor(622)
#passMCsubtractMerge.GetXaxis().SetTitle("DDB")
#passMCsubtractMerge.GetYaxis().SetTitle("Events/Bin")
leg2.AddEntry(passMCsubtractMerge, "t#bar{t} mistag", "f")
subtractedDataMerge.SetLineColor(1)
subtractedDataMerge.SetMarkerStyle(20)
subtractedDataMerge.SetMarkerSize(1.5)
#subtractedDataMerge.GetXaxis().SetTitle("DDB")
#subtractedDataMerge.GetYaxis().SetTitle("Events/Bin")
leg2.AddEntry(subtractedDataMerge, "Subtracted Data", "lep")
failMCsubtractMerge.Draw("hist")
passMCsubtractMerge.Draw("histsame")
subtractedDataMerge.Draw("e1same")
leg2.Draw()
lt2 = TLatex()
if ana_ == "Inclusive":
lt2.DrawLatexNDC(0.17, 0.92, "#scale[0.7]{#bf{" + ana_ + "}}")
if ana_ == "PT-200-350" or ana_ == "PT-350-500" or ana_ == "PT-500-2000":
words = ana_.split("-")
if words[2] == "2000":
lt2.DrawLatexNDC(
0.17, 0.92, "#scale[0.7]{#bf{p_{T} " + words[1] + "-Inf GeV}}")
else:
lt2.DrawLatexNDC(
0.17, 0.92, "#scale[0.7]{#bf{p_{T} " + words[1] + "-" +
words[2] + " GeV}}")
else:
words = ana_.split("-")
lt2.DrawLatexNDC(
0.17, 0.92, "#scale[0.7]{#bf{" + words[0] + " " + words[1] + "-" +
words[2] + " GeV}}")
lt2.DrawLatexNDC(0.23, 0.85,
"#scale[0.8]{CMS} #scale[0.65]{#bf{#it{Internal}}}")
lt2.DrawLatexNDC(0.23, 0.8, "#scale[0.7]{#bf{t#bar{t} CR (e+#mu)}}")
lt2.DrawLatexNDC(0.23, 0.75, "#scale[0.5]{#bf{2-prong (bq) enriched}}")
if year_ == 2017:
lt2.DrawLatexNDC(0.71, 0.92, "#scale[0.7]{#bf{41.5 fb^{-1} (13 TeV)}}")
if year_ == 2018:
lt2.DrawLatexNDC(0.71, 0.92,
"#scale[0.7]{#bf{58.827 fb^{-1} (13 TeV)}}")
c2.SaveAs(dir + "Merged_subtrac.pdf") #
#----------------------- canvas 3 -----------------------#
c3 = myCanvas(c="c3", size=[700, 900])
pad1 = TPad("pad1", "", 0.01, 0.25, 0.93, 1.0)
pad1.SetTopMargin(0.1)
pad1.SetRightMargin(0.05)
pad1.SetLeftMargin(0.17)
pad1.SetBottomMargin(0.05)
pad2 = TPad("pad2", "", 0.0, 0.0, 0.375, 0.24)
pad2.SetTopMargin(0.0)
pad2.SetRightMargin(0.0)
pad2.SetLeftMargin(0.0)
pad2.SetBottomMargin(0.0)
pad3 = TPad("pad3", "", 0.38, 0.025, 0.94, 0.25)
pad2.SetTopMargin(0.05)
pad2.SetRightMargin(0.0)
pad2.SetLeftMargin(0.45)
pad2.SetBottomMargin(0.2)
pad1.Draw()
pad2.Draw()
pad3.Draw()
#* Pad 1 *#
pad1.cd()
leg3 = myLegend(coordinate=[0.65, 0.4, 0.75, 0.5])
xaxisname = arr.array('d', [1, 2])
zero1 = np.zeros(2)
gPad.Modified()
gPad.SetGridy()
gr1 = TGraphErrors(2, xaxisname, ttMC_fraction, zero1, ttMC_error)
gr1.SetTitle("t#bar{t}")
gr1.SetLineColor(870)
gr1.SetLineWidth(3)
gr1.SetMarkerStyle(20)
gr1.SetMarkerColor(870)
leg3.AddEntry(gr1, "t#bar{t}", "lep")
gr2 = TGraphErrors(2, xaxisname, ttData_fraction, zero1, ttData_error)
gr2.SetTitle("t#bar{t} Data")
gr2.SetLineColor(1)
gr2.SetLineWidth(2)
gr2.SetMarkerStyle(20)
gr2.SetMarkerColor(1)
leg3.AddEntry(gr2, "t#bar{t} Data", "lep")
mg = TMultiGraph("mg", "")
mg.Add(gr1)
mg.Add(gr2)
mg.GetHistogram().SetMaximum(1.5)
mg.GetHistogram().SetMinimum(0)
mg.GetYaxis().SetTitle("Fraction")
mg.GetXaxis().SetLimits(0, 3)
mg.GetXaxis().SetTickLength(0.03)
mg.GetXaxis().SetNdivisions(103)
mg.GetXaxis().ChangeLabel(2, -1, -1, -1, -1, -1, "Fail")
mg.GetXaxis().ChangeLabel(1, -1, 0)
mg.GetXaxis().ChangeLabel(-1, -1, 0)
mg.GetXaxis().ChangeLabel(3, -1, -1, -1, -1, -1, "Pass")
mg.Draw("AP")
leg3.Draw()
lt3 = TLatex()
if ana_ == "Inclusive":
lt3.DrawLatexNDC(0.17, 0.92, "#scale[0.7]{#bf{" + ana_ + "}}")
if ana_ == "PT-200-350" or ana_ == "PT-350-500" or ana_ == "PT-500-2000":
words = ana_.split("-")
if words[2] == "2000":
lt3.DrawLatexNDC(
0.17, 0.92, "#scale[0.7]{#bf{p_{T} " + words[1] + "-Inf GeV}}")
else:
lt3.DrawLatexNDC(
0.17, 0.92, "#scale[0.7]{#bf{p_{T} " + words[1] + "-" +
words[2] + " GeV}}")
else:
words = ana_.split("-")
lt3.DrawLatexNDC(
0.17, 0.92, "#scale[0.7]{#bf{" + words[0] + " " + words[1] + "-" +
words[2] + " GeV}}")
lt3.DrawLatexNDC(0.19, 0.855,
"#scale[0.8]{CMS} #scale[0.65]{#bf{#it{Internal}}}")
lt3.DrawLatexNDC(0.19, 0.805, "#scale[0.7]{#bf{t#bar{t} CR (e+#mu)}}")
lt3.DrawLatexNDC(0.19, 0.755, "#scale[0.5]{#bf{2-prong (bq) enriched}}")
if year_ == 2017:
lt3.DrawLatexNDC(0.71, 0.92, "#scale[0.7]{#bf{41.5 fb^{-1} (13 TeV)}}")
if year_ == 2018:
lt3.DrawLatexNDC(0.71, 0.92,
"#scale[0.7]{#bf{58.827 fb^{-1} (13 TeV)}}")
lt3.Draw()
pad1.Update()
#* Pad 2 *#
pad2.cd()
MCpassMerge.SetFillColor(0)
MCpassMerge.SetLineColor(870)
MCpassMerge.SetLineWidth(3)
MCpassMerge.SetMarkerColor(870)
MCpassMerge.SetMarkerStyle(20)
MCpassMerge.GetYaxis().SetTitle("Fraction")
MCpassMerge.GetYaxis().SetTitleSize(0.09)
MCpassMerge.GetYaxis().SetLabelSize(0.1)
MCpassMerge.GetYaxis().SetNdivisions(404)
MCpassMerge.SetMaximum(0.3)
MCpassMerge.SetMinimum(0.0)
MCpassMerge.GetXaxis().SetTitle("")
MCpassMerge.GetXaxis().SetLabelOffset(0.02)
MCpassMerge.GetXaxis().SetLabelSize(0.09)
MCpassMerge.GetXaxis().SetNdivisions(104)
MCpassMerge.GetXaxis().ChangeLabel(2, -1, -1, -1, -1, -1, "Pass")
MCpassMerge.GetXaxis().ChangeLabel(1, -1, 0)
MCpassMerge.GetXaxis().ChangeLabel(-1, -1, 0)
datapassMerge.SetFillColor(0)
datapassMerge.SetLineColor(1)
datapassMerge.SetLineWidth(2)
datapassMerge.SetMarkerColor(1)
datapassMerge.SetMarkerStyle(20)
MCpassMerge.Draw("e1")
datapassMerge.Draw("e1histsame")
#* Pad 3 *#
pad3.cd()
SF = sfMerge.Integral()
print "******"
print "mistag SF:", SF
SFfinal = round(SF, 3)
SFtext = "SF = " + str(SFfinal)
mistagSFmax = SF + 0.2
mistagSFmin = SF - 0.2
sfMerge.SetLineColor(797)
sfMerge.SetMarkerColor(797)
sfMerge.SetLineWidth(3)
sfMerge.SetMaximum(mistagSFmax)
sfMerge.SetMinimum(mistagSFmin)
sfMerge.GetXaxis().SetTitle(" ")
sfMerge.GetXaxis().SetLabelOffset(999)
sfMerge.GetXaxis().SetLabelSize(0)
sfMerge.GetXaxis().SetTickLength(0)
sfMerge.GetYaxis().SetLabelSize(0.1)
sfMerge.GetYaxis().SetNdivisions(404)
sfMerge.GetYaxis().SetTitle(" ")
sfMerge.Draw("e1hist")
pt = TPaveText(0.21, 0.72, 0.31, 0.8, "brNDC")
pt.SetBorderSize(0)
pt.SetTextAlign(12)
pt.SetFillStyle(0)
pt.SetTextFont(42)
pt.SetTextSize(0.1)
pt.AddText(SFtext)
pt.Draw()
c3.SaveAs(dir + "Merge_SF.pdf") #
passedBfrSubtactDataMerge = (passedTopEdataBfrSubtract +
passedTopMudataBfrSubtract).Integral()
failedBfrSubtractDataMerge = (failedTopEdataBfrSubtract +
failedTopMudataBfrSubtract).Integral()
passbackground = (wjetsTopEpassed + wjetsTopMupassed + dibosonTopEpassed +
dibosonTopMupassed + stTopEpassed +
stTopMupassed).Integral()
failbackground = (wjetsTopEfailed + wjetsTopMufailed + dibosonTopEfailed +
dibosonTopMufailed + stTopEfailed +
stTopMufailed).Integral()
totalbackground = (wjetsMerge + dibosonMerge + stMerge).Integral()
#get the statistical uncertainty#
dx = ttData_error[1]
print "data efficiency error", dx
dy = ttMC_error[1]
print "MC efficiency error", dy
x = datapassMerge.Integral()
y = MCpassMerge.Integral()
statUnc = TMath.Sqrt(((dx**2) / (y**2)) + ((x**2) * (dy**2) / (y**4)))
#print "statistical Uncertainty in Top (e+muon) CR", statUnc
#print " "
print "relative statistical Uncertainty in Top (e+muon) CR", statUnc / SF * 100, " %"
print " "
print "DDB Mistag SF and stat: ", round(SF, 3), " +- ", round(statUnc,
3), " (stat)"
#print "theoretical statistical uncertainty of data efficiency", TMath.Sqrt((x*(1-x))/(subtractedDataMerge.Integral()))
#print "theoretical statistical uncertainty of MC efficiency", TMath.Sqrt((y*(1-y))/(totalMCmerge.Integral()))
#print " "
header = ["Process", "Number of Events", "Top (e+muon)"]
row1 = [
" ", "DDB mistag SF",
str(SFfinal) + " +- " + str(round(statUnc, 3)) + " (stat)"
]
row2 = ["tt MC", "Pass (not normalized)", ""]
row3 = [
" ", "Pass (normalized)",
str(round(passMCsubtractMerge.Integral(), 2))
]
row4 = [" ", "Fail (not normalized)", ""]
row5 = [
" ", "Fail (normalized)",
str(round(failMCsubtractMerge.Integral(), 2))
]
row6 = [" ", "Total (not normalized)", ""]
row7 = [" ", "Total (normalized)", str(round(totalMCmerge.Integral(), 2))]
inforMC = [row2, row3, row4, row5, row6, row7]
row8 = [
"tt DATA", "Pass (before subtraction)",
str(round(passedBfrSubtactDataMerge, 2))
]
row9 = [" ", "Pass (after subtraction)", str(round(passdata, 2))]
row10 = [
" ", "Fail (before subtraction)",
str(round(failedBfrSubtractDataMerge, 2))
]
row11 = [" ", "Fail (after subtraction)", str(round(faildata, 2))]
row12 = [
" ", "Total (before subtraction)",
str(round(unsubtractedDataMerge.Integral(), 2))
]
row13 = [" ", "Total (after subtraction)", str(round(totaldata, 2))]
inforDATA = [row8, row9, row10, row11, row12, row13]
row14 = ["Background", "Pass (normalized)", str(round(passbackground, 2))]
row15 = [" ", "Fail (normalized)", str(round(failbackground, 2))]
row16 = [" ", "Total (normalized)", str(round(totalbackground, 2))]
inforBKG = [row14, row15, row16]
DDB_mistagSF.makeTable(header, row1, inforMC, inforDATA, inforBKG)
galpha2.GetYaxis().SetRangeUser(0., 2.)
c1.cd(6)
gslope2.Draw("APL")
islope2.Draw("P, SAME")
drawRegion(category)
gslope2.GetYaxis().SetRangeUser(0., 20.)
c1.Print(PLOTDIR+"MC_signal_"+YEAR+"/"+stype+"_"+category+"_SignalShape.pdf")
c1.Print(PLOTDIR+"MC_signal_"+YEAR+"/"+stype+"_"+category+"_SignalShape.png")
c2 = TCanvas("c2", "Signal Efficiency", 800, 600)
c2.cd(1)
gnorm.SetMarkerColor(cColor)
gnorm.SetMarkerStyle(20)
gnorm.SetLineColor(cColor)
gnorm.SetLineWidth(2)
gnorm.Draw("APL")
inorm.Draw("P, SAME")
gnorm.GetXaxis().SetRangeUser(genPoints[0]-100, genPoints[-1]+100)
gnorm.GetYaxis().SetRangeUser(0., gnorm.GetMaximum()*1.25)
#drawCMS(-1, "Simulation Preliminary", year=YEAR)
#drawCMS(-1, "Work in Progress", year=YEAR, suppressCMS=True)
drawCMS(-1, "", year=YEAR, suppressCMS=True)
drawAnalysis(category)
drawRegion(category)
c2.Print(PLOTDIR+"MC_signal_"+YEAR+"/"+stype+"_"+category+"_SignalNorm.pdf")
c2.Print(PLOTDIR+"MC_signal_"+YEAR+"/"+stype+"_"+category+"_SignalNorm.png")
#*******************************************************#
# #
def plot_2(var, cuts):
for s in attr:
if ct_dep == 0:
c1 = TCanvas("c1", "Signals", 800, 800)
c1.cd()
c1.SetGrid()
#gStyle.SetTitleFontSize(8.1)
if s in ('elf', 'muf', 'chm', 'cm', 'nm'):
c1.SetLogx()
for cc in channel:
hist[cc][s].Draw('colz same')
hist_CHS[cc][s].Draw('colz same')
legend = TLegend(0.90, 0.90, 0.99, 0.99)
#legend.SetHeader('Samples')
for cc in channel:
legend.AddEntry(hist[cc][s], cc)
legend.AddEntry(hist_CHS[cc][s], cc + 'CHS')
legend.Draw()
c1.Print(path1 + s + var +
cuts.replace('(', '_').replace(')', '_').replace(
'&&', 'A').replace('>', 'LG').replace('<', 'LS').
replace('=', 'EQ').replace('.', 'P').replace('-', 'N').
replace('Jet', 'J').replace('GenBquark', 'GBQ') + ".pdf")
c1.Update()
c1.Close()
print('|||||||||||||||||||||||||||||||||||||||||||||||||||')
elif ct_dep == 1:
eac0 = str(entries_after_cut['ct0'][s])
c1 = TCanvas("c1", "Signals", 800, 800)
c1.SetGrid()
c1.cd()
c1.SetLogx()
#gr = TGraph( len_of_lt , x , yy['sgn'][s] )
gr = TGraphErrors(len_of_lt, x, yy['sgn'][s], ex, ey['sgn'][s])
gr.SetMarkerSize(1.5)
gr.SetMarkerStyle(21)
gr.SetLineColor(4)
gr.SetLineWidth(4)
gr.SetTitle(
'averaged ' + s + ' cut: ' +
cuts.replace('(', '_').replace(')', '_').replace('&&', ',').
replace('Jet', 'J').replace('GenBquark', 'GBQ') + '[entries:' +
eac0 + ']')
gr.GetXaxis().SetTitle('decaying length (mm)')
gr.GetYaxis().SetTitle('mean frequency')
gr.SetName('sgn')
gr.Draw('ACP') # '' sets up the scattering style
gr1 = TGraphErrors(len_of_lt, x, yy['qcd'][s], ex, ey['qcd'][s])
gr1.SetMarkerSize(1.0)
gr1.SetMarkerStyle(2)
gr1.SetLineColor(2)
gr1.SetLineWidth(2)
gr1.SetName('qcd')
#gr1.SetTitle('averaged ' + s)
#gr1.GetXaxis().SetTitle('decaying length (mm)')
#gr1.GetYaxis().SetTitle('mean frequency')
gr1.Draw('CP') # '' sets up the scattering style
legend = TLegend(0.89, 0.89, 0.99, 0.99)
legend.AddEntry('qcd', legendb)
legend.AddEntry('sgn', legends)
legend.Draw()
c1.Print(path1 + 'mean_' + s + var +
cuts.replace('(', '_').replace(')', '_').replace(
'&&', 'A').replace('>', 'LG').replace('<', 'LS').
replace('=', 'EQ').replace('.', 'P').replace('-', 'N').
replace('Jet', 'J').replace('GenBquark', 'GBQ') + ".pdf")
c1.Update()
c1.Close()
print('|||||||||||||||||||||||||||||||||||||||||||||||||||')
def makePlot1D(filepath, foutname, plottitle='', masstitle=''):
br = 1 if 'Resonant' in plottitle else 0.68
limits = parseLimitFiles2D(filepath, br)
xaxis = []
xseclist = []
xsecerr = []
cent = []
obs = []
up1 = []
up2 = []
down1 = []
down2 = []
maxval = 0
minval = 999
for m in sorted(limits):
l = limits[m]
xaxis.append(m)
xseclist.append(l.xsec)
xsecerr.append(l.xsec * .2)
cent.append(l.cent)
up1.append(l.up1 - l.cent)
up2.append(l.up2 - l.cent)
down1.append(l.cent - l.down1)
down2.append(l.cent - l.down2)
obs.append(l.obs)
maxval = max(maxval, l.up2)
minval = min(minval, l.down2)
N = len(xaxis)
up1Sigma = array('f', up1)
up2Sigma = array('f', up2)
down1Sigma = array('f', down1)
down2Sigma = array('f', down2)
cent = array('f', cent)
obs = array('f', obs)
xarray = array('f', xaxis)
xsecarray = array('f', xseclist)
xsecerrarray = array('f', xsecerr)
zeros = array('f', [0 for i in xrange(N)])
graphXsec = TGraphErrors(N, xarray, xsecarray, zeros, xsecerrarray)
graphCent = TGraph(N, xarray, cent)
graphObs = TGraph(N, xarray, obs)
graph1Sigma = TGraphAsymmErrors(N, xarray, cent, zeros, zeros, down1Sigma,
up1Sigma)
graph2Sigma = TGraphAsymmErrors(N, xarray, cent, zeros, zeros, down2Sigma,
up2Sigma)
c = TCanvas('c', 'c', 700, 600)
c.SetLogy()
c.SetLeftMargin(.15)
graph2Sigma.GetXaxis().SetTitle(masstitle + ' [GeV]')
graph2Sigma.GetYaxis().SetTitle(
'95% C.L. upper limit [#sigma/#sigma_{theory}]')
c2 = root.kOrange
c1 = root.kGreen + 1
graph2Sigma.SetLineColor(c2)
graph1Sigma.SetLineColor(c1)
graph2Sigma.SetFillColor(c2)
graph1Sigma.SetFillColor(c1)
graph2Sigma.SetMinimum(0.5 * minval)
graph2Sigma.SetMaximum(10 * maxval)
graphCent.SetLineWidth(2)
graphCent.SetLineStyle(2)
graphObs.SetLineColor(1)
graphObs.SetLineWidth(3)
graphObs.SetMarkerStyle(20)
graphObs.SetMarkerSize(1)
graphObs.SetMarkerColor(1)
graph1Sigma.SetLineStyle(0)
graph2Sigma.SetLineStyle(0)
leg = TLegend(0.55, 0.7, 0.9, 0.9)
leg.AddEntry(graphCent, 'Expected', 'L')
if not BLIND:
leg.AddEntry(graphObs, 'Observed', 'Lp')
leg.AddEntry(graph1Sigma, '1 std. dev.', 'F')
leg.AddEntry(graph2Sigma, '2 std. dev.', 'F')
leg.SetFillStyle(0)
leg.SetBorderSize(0)
graph2Sigma.Draw('A3')
graph1Sigma.Draw('3 same')
graphCent.Draw('same L')
if not BLIND:
graphObs.Draw('same Lp')
subscript = 'SR' if 'Resonant' in plottitle else 'FC'
coupling = '0.1' if 'Resonant' in plottitle else '0.25'
graphXsec.SetLineColor(2)
graphXsec.SetLineWidth(2)
graphXsec.SetLineStyle(2)
graphXsec.SetFillColor(2)
graphXsec.SetFillStyle(3005)
graphXsec.Draw('same L3')
'''
if not scale:
if 'Resonant' in plottitle:
leg.AddEntry(graphXsec,'Theory #splitline{a_{%s}=b_{%s}=%s}{m_{#chi}=100 GeV}'%(subscript,subscript,coupling),'l')
else:
leg.AddEntry(graphXsec,'Theory #splitline{a_{%s}=b_{%s}=%s}{m_{#chi}=10 GeV}'%(subscript,subscript,coupling),'l')
'''
if not BLIND:
findIntersect1D(graphObs, graphXsec, xaxis[0], xaxis[-1])
findIntersect1D(graphCent, graphXsec, xaxis[0], xaxis[-1])
leg.Draw()
label = TLatex()
label.SetNDC()
label.SetTextSize(0.8 * c.GetTopMargin())
label.SetTextFont(62)
label.SetTextAlign(11)
label.DrawLatex(0.15, 0.94, "CMS")
label.SetTextFont(52)
label.SetTextSize(0.6 * c.GetTopMargin())
# label.DrawLatex(0.25,0.94,"Preliminary")
label.SetTextFont(42)
label.SetTextSize(0.7 * c.GetTopMargin())
label.DrawLatex(0.19, 0.83, plottitle)
if 'Resonant' in plottitle:
label.DrawLatex(0.19, 0.75, "a_{SR} = b_{SR} = %s" % coupling)
label.DrawLatex(0.19, 0.68, "m_{#chi}=100 GeV")
else:
label.DrawLatex(0.19, 0.75, "g_{DM}^{V}=1,g_{q}^{V}=0.25")
label.DrawLatex(0.19, 0.68, "m_{#chi}=1 GeV")
label.SetTextSize(0.6 * c.GetTopMargin())
label.SetTextFont(42)
label.SetTextAlign(31) # align right
label.DrawLatex(0.95, 0.94, "%.1f fb^{-1} (13 TeV)" % (plotConfig.lumi))
c.SaveAs(foutname + '.pdf')
c.SaveAs(foutname + '.png')
H8_Projection_40TeV.Sumw2()
H8_Projection_40TeV.Scale(1 / H8_Projection_40TeV.Integral())
Fraction_WW_Use = H8_Projection_40TeV.GetBinContent(Sort_particle)
Fraction_WW_40TeV.append(Fraction_WW_Use)
Y_Error_WW_40TeV.append(
np.sqrt(H8_Projection_40TeV.GetEntries() * Fraction_WW_Use *
(1 - Fraction_WW_Use)) /
H8_Projection_40TeV.GetEntries())
else:
Y_Error_WW_40TeV.append(0)
Fraction_WW_40TeV.append(0)
gr_QQ_5TeV = TGraphErrors(4, X_PT, Fraction_QQ_5TeV, X_Error,
Y_Error_QQ_5TeV)
gr_QQ_5TeV.SetLineColor(1)
gr_QQ_5TeV.SetLineWidth(1)
gr_QQ_5TeV.SetLineStyle(1)
gr_QQ_5TeV.SetMarkerColor(1)
gr_QQ_5TeV.SetMarkerStyle(8)
gr_QQ_5TeV.SetMarkerSize(1)
gr_QQ_5TeV.GetXaxis().SetTitle("Log(PT)")
gr_QQ_5TeV.GetYaxis().SetTitle("Arbitrary")
gr_WW_5TeV = TGraphErrors(4, X_PT, Fraction_WW_5TeV, X_Error,
Y_Error_WW_5TeV)
gr_WW_5TeV.SetLineColor(2)
gr_WW_5TeV.SetLineWidth(1)
gr_WW_5TeV.SetLineStyle(1)
gr_WW_5TeV.SetMarkerColor(2)
gr_WW_5TeV.SetMarkerStyle(8)
gr_WW_5TeV.SetMarkerSize(1)
leg.AddEntry(jetRatePlot, "Jets", "l") leg.AddEntry(jetToMETRatePlot, "MET", "l") leg.AddEntry(jetToElectronRatePlot, "Electrons", "l") leg.AddEntry(jetToMuonRatePlot, "Muons", "l") ## Removing stat info #jetRatePlot.SetStats(False) #jetToMuonRatePlot.SetStats(False) #jetToElectronRatePlot.SetStats(False) ## jetToPhotonRatePlot.SetStats(False) #jetToMETRatePlot.SetStats(False) # Plotting stuff ratePlot.Add(jetRatePlot, "A3L") jetRatePlot.SetFillStyle(1001) jetRatePlot.SetLineWidth(2) jetRatePlot.SetLineStyle(1) ratePlot.Add(jetToMuonRatePlot, "A3") jetToMuonRatePlot.SetFillStyle(1001) jetToMuonRatePlot.SetLineWidth(2) jetToMuonRatePlot.SetLineStyle(1) ratePlot.Add(jetToElectronRatePlot, "A3") jetToElectronRatePlot.SetFillStyle(1001) jetToElectronRatePlot.SetLineWidth(2) jetToElectronRatePlot.SetLineStyle(1) # ratePlot.Add(jetToPhotonRatePlot, "A3") # jetToPhotonRatePlot.SetFillStyle(1001) # jetToPhotonRatePlot.SetLineWidth(2) # jetToPhotonRatePlot.SetLineStyle(1) ratePlot.Add(jetToMETRatePlot, "A3") jetToMETRatePlot.SetFillStyle(1001)
def draw(hist,
var_type,
log='',
plotdir=None,
plotname='foop',
more_hists=None,
write_csv=False,
stats=None,
bounds=None,
errors=False,
shift_overflows=False,
csv_fname=None,
scale_errors=None,
rebin=None,
plottitle='',
colors=None,
linestyles=None,
cwidth=None,
cheight=None,
imagetype='svg',
xtitle=None,
ytitle=None,
xline=None,
yline=None,
draw_str=None,
normalize=False,
normalization_bounds=None,
linewidths=None,
markersizes=None,
no_labels=False,
graphify=False,
translegend=(0.0, 0.0)):
assert os.path.exists(plotdir)
if not has_root:
return
if normalization_bounds is not None:
assert bounds is None
if bounds is not None:
assert normalization_bounds is None
cvn = TCanvas('cvn-' + plotname, '', 700 if cwidth is None else cwidth,
600 if cheight is None else cheight)
hists = [
hist,
]
if more_hists != None:
hists = hists + more_hists
xmin, xmax, ymax = None, None, None
ih = 0
for htmp in hists:
if rebin is not None:
htmp.Rebin(rebin)
if scale_errors is not None:
factor = float(
scale_errors[0]) if len(scale_errors) == 1 else float(
scale_errors[ih])
for ibin in range(htmp.GetNbinsX() + 2):
htmp.SetBinError(ibin, htmp.GetBinError(ibin) * factor)
if not normalize:
assert normalization_bounds is None
if bounds is not None:
ibin_start = htmp.FindBin(bounds[0])
ibin_end = htmp.FindBin(bounds[1])
this_y_max = GetMaximumWithBounds(
htmp,
htmp.GetXaxis().GetBinLowEdge(ibin_start),
htmp.GetXaxis().GetBinUpEdge(ibin_end))
else:
this_y_max = htmp.GetMaximum()
else: # renormalize the hist within these bounds
if normalization_bounds is None:
factor = 1. / htmp.Integral() if htmp.Integral() > 0.0 else 0.0
htmp.Scale(factor)
this_y_max = htmp.GetMaximum()
else:
ibin_start = 0 if normalization_bounds[
0] is None else htmp.FindBin(normalization_bounds[0])
ibin_end = htmp.GetNbinsX(
) if normalization_bounds[1] is None else htmp.FindBin(
normalization_bounds[1])
factor = htmp.Integral(ibin_start, ibin_end) if htmp.Integral(
ibin_start, ibin_end
) > 0.0 else 0.0 # NOTE this is inclusive, i.e. includes <ibin_end>
htmp.Scale(factor)
this_y_max = GetMaximumWithBounds(
htmp,
htmp.GetXaxis().GetBinLowEdge(ibin_start),
htmp.GetXaxis().GetBinUpEdge(ibin_end))
if ymax is None or this_y_max > ymax:
ymax = this_y_max
if xmin is None or htmp.GetBinLowEdge(1) < xmin:
xmin = htmp.GetBinLowEdge(1)
if xmax is None or htmp.GetXaxis().GetBinUpEdge(
htmp.GetNbinsX()) > xmax:
xmax = htmp.GetXaxis().GetBinUpEdge(htmp.GetNbinsX())
ih += 1
if bounds is not None:
xmin, xmax = bounds
hframe = TH1D('hframe', '', hist.GetNbinsX(), xmin, xmax)
if not no_labels and (var_type == 'string' or var_type == 'bool'):
for ib in range(1, hframe.GetNbinsX() + 1):
hframe.GetXaxis().SetBinLabel(ib, hist.GetXaxis().GetBinLabel(ib))
if 'y' in log:
hframe.SetMaximum(3 * ymax)
else:
hframe.SetMaximum(1.2 * ymax)
if var_type == 'bool':
hframe.GetXaxis().SetLabelSize(0.1)
if plottitle == '':
plottitle = plotname
if xtitle is None:
xtitle = hist.GetXaxis().GetTitle()
if ytitle is None:
ytitle = hframe.GetYaxis().GetTitle()
hframe.SetTitle(plottitle + ';' + xtitle + ';' + ytitle)
# gStyle.SetTitleFontSize(.075)
# gStyle.SetTitleY(gStyle.GetTitleY() + .0004)
if cwidth is not None:
gStyle.SetTitleOffset(0.99 * gStyle.GetTitleOffset('y'), 'y')
# gStyle.SetTitleFillStyle(0)
# hframe.SetTitleSize(gStyle.GetTitleSize())
# hframe.SetTitleFont(gStyle.GetTitleFont())
hframe.Draw('txt')
if shift_overflows:
for htmp in hists:
if htmp == None:
continue
underflows, overflows = 0.0, 0.0
first_shown_bin, last_shown_bin = -1, -1
for ib in range(0, htmp.GetXaxis().GetNbins() + 2):
if htmp.GetXaxis().GetBinCenter(ib) <= xmin:
underflows += htmp.GetBinContent(ib)
htmp.SetBinContent(ib, 0.0)
elif first_shown_bin == -1:
first_shown_bin = ib
else:
break
for ib in reversed(range(0, htmp.GetXaxis().GetNbins() + 2)):
if htmp.GetXaxis().GetBinCenter(ib) >= xmax:
overflows += htmp.GetBinContent(ib)
htmp.SetBinContent(ib, 0.0)
elif last_shown_bin == -1:
last_shown_bin = ib
else:
break
if 'd_hamming' in plotname:
print htmp.GetTitle()
print ' underflow', underflows, htmp.GetBinContent(
first_shown_bin)
print ' overflow', overflows, htmp.GetBinContent(
last_shown_bin)
print ' first', htmp.GetXaxis().GetBinCenter(first_shown_bin)
print ' last', htmp.GetXaxis().GetBinCenter(last_shown_bin)
htmp.SetBinContent(
first_shown_bin,
underflows + htmp.GetBinContent(first_shown_bin))
htmp.SetBinContent(last_shown_bin,
overflows + htmp.GetBinContent(last_shown_bin))
if colors is None:
assert len(hists) < 5
colors = (kRed, kBlue - 4, kGreen + 2, kOrange + 1
) # 632, 596, 418, 801
else:
assert len(hists) <= len(colors)
if linestyles is None:
# assert len(hists) < 5
linestyles = [1 for _ in range(len(hists))]
else:
assert len(hists) <= len(linestyles)
# legends
x0, y0, x1, y1 = 0.57 + translegend[0], 0.66 + translegend[
1], 0.99 + translegend[0], 0.88 + translegend[1]
if len(hists) < 5:
leg = TLegend(x0, y0, x1, y1)
else:
leg = TLegend(x0, y0 - 0.05, x1, y1)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
# draw
if graphify:
graphs = []
for ih in range(len(hists)):
htmp = hists[ih]
n_bins = hists[ih].GetNbinsX()
xvals, yvals, xerrs, yerrs = array(
'f', [0 for i in range(n_bins)]), array(
'f', [0 for i in range(n_bins)]), array(
'f', [0 for i in range(n_bins)]), array(
'f', [0 for i in range(n_bins)])
for ib in range(1, n_bins + 1): # NOTE ignoring overflows
xvals[ib - 1] = hists[ih].GetXaxis().GetBinCenter(ib)
xerrs[ib - 1] = 0.0
yvals[ib - 1] = hists[ih].GetBinContent(ib)
yerrs[ib - 1] = hists[ih].GetBinError(ib) if errors else 0.0
gr = TGraphErrors(n_bins, xvals, yvals, xerrs, yerrs)
if markersizes is not None:
imark = ih if len(markersizes) > 1 else 0
gr.SetMarkerSize(markersizes[imark])
gr.SetMarkerColor(colors[ih])
if linewidths is None:
if ih < 6: # and len(hists) < 5:
gr.SetLineWidth(6 - ih)
else:
ilw = ih if len(linewidths) > 1 else 0
gr.SetLineWidth(linewidths[ilw])
gr.SetLineColor(colors[ih])
# if int(linewidth) == 1:
# gr.SetLineColorAlpha(colors[ih], 0.4)
gr.SetLineStyle(linestyles[ih])
if draw_str is None:
draw_str = 'lpz'
if hists[ih].Integral() != 0.0:
gr.Draw(draw_str + ' same')
statstr = ''
if stats is not None:
if 'rms' in stats:
statstr += ' (%.2f)' % htmp.GetRMS()
if 'mean' in stats:
statstr += ' (%.2f)' % htmp.GetMean()
if '0-bin' in stats:
statstr += ' (%.2f)' % htmp.GetBinContent(1)
leg.AddEntry(gr, hists[ih].GetTitle() + ' ' + statstr, 'pl')
graphs.append(
gr
) # yes, you really do have to do this to keep root from giving you only one graph
else:
if draw_str is None:
draw_str = 'hist same'
else:
draw_str += ' same'
if errors:
draw_str += ' e'
for ih in range(len(hists)):
htmp = hists[ih]
if stats is not None:
if 'rms' in stats:
htmp.SetTitle(htmp.GetTitle() +
(' (%.2f)' % htmp.GetRMS()))
if 'mean' in stats:
htmp.SetTitle(htmp.GetTitle() +
(' (%.2f)' % htmp.GetMean()))
if '0-bin' in stats:
htmp.SetTitle(htmp.GetTitle() +
(' (%.2f)' % htmp.GetBinContent(1)))
htmp.SetLineColor(colors[ih])
if markersizes is not None:
imark = ih if len(markersizes) > 1 else 0
htmp.SetMarkerSize(markersizes[imark])
htmp.SetMarkerColor(colors[ih])
htmp.SetLineStyle(linestyles[ih])
if linewidths is None:
if ih < 6: # and len(hists) < 5:
htmp.SetLineWidth(6 - ih)
else:
ilw = ih if len(linewidths) > 1 else 0
htmp.SetLineWidth(linewidths[ilw])
leg.AddEntry(htmp, htmp.GetTitle(), 'l')
htmp.Draw(draw_str)
leg.Draw()
if xline is not None:
# if xline < hframe.GetXaxis().GetXmin() or xline > hframe.GetXaxis().GetXmax(): # make sure we got valid a x position for the line
# print 'WARNING plotting x line at %f out of bounds (%f, %f)' % (float(xmin), hframe.GetXaxis().GetXmin(), hframe.GetXaxis().GetXmax())
# xl = TLine(xline, hframe.GetYaxis().GetXmin(), xline, 0.5*ymax)
xl = TLine(xline, -0.1 * ymax, xline, 0.5 * ymax)
xl.SetLineStyle(2)
xl.Draw()
if yline is not None:
# if yline < hframe.GetYaxis().GetXmin() or xline > hframe.GetYaxis().GetXmax(): # make sure we got valid a x position for the line
# print 'WARNING plotting y line at %f out of bounds (%f, %f)' % (float(ymin), hframe.GetYaxis().GetXmin(), hframe.GetYaxis().GetXmax())
yl = TLine(hframe.GetXaxis().GetXmin(), yline,
hframe.GetXaxis().GetXmax(), yline)
yl.Draw()
cvn.SetLogx('x' in log)
cvn.SetLogy('y' in log)
if not os.path.exists(plotdir + '/plots'):
print 'ERROR dir \'' + plotdir + '/plots\' d.n.e.'
assert False
if write_csv:
assert more_hists == None
if csv_fname == None:
write_hist_to_file(plotdir + '/plots/' + plotname + '.csv', hist)
else:
write_hist_to_file(csv_fname, hist)
cvn.SaveAs(plotdir + '/plots/' + plotname + '.' + imagetype)
continue
title = fLine[9:]
#define some data points . . .
x = array('f', kineticEnergy)
y = array('f', crossSec)
exl = array('f', zero)
exr = array('f', zero)
nPoints = len(x)
# . . . and hand over to TGraphErros object
gr = TGraphErrors(nPoints, x, y, exl, exr)
gr.SetTitle(title + "; Kinetic Energy [MeV]; Cross Section [barn]")
gr.GetXaxis().SetRangeUser(0, 1000)
gr.GetYaxis().SetRangeUser(0, 2.)
gr.SetLineWidth(2)
gr.SetLineColor(kGreen - 2)
gr.SetFillColor(0)
## Data
data_FileName = "/Volumes/Seagate/Elena/TPC/Data60A.root"
data_File = TFile.Open(data_FileName)
interactingPlotString = "RecoXS/hRecoInteractingKE"
incidentPlotString = "RecoXS/hRecoIncidentKE"
data_Int = data_File.Get(interactingPlotString)
data_Inc = data_File.Get(incidentPlotString)
XSDataRecoPion = data_Int.Clone("pionMCXSData")
XSDataRecoPion.Sumw2()
data_Inc.Sumw2()
XSDataRecoPion.Scale(101.10968)
XSDataRecoPion.Divide(data_Inc)