def extract_MET(f_tt, f_qcd, f_wjet):
# get trees from files
t_tt = f_tt.Get("Delphes")
t_qcd = f_qcd.Get("Delphes")
t_wjet = f_wjet.Get("Delphes")
# get number of entries
tt_n_entries = t_tt.GetEntries()
qcd_n_entries = t_qcd.GetEntries()
wjet_n_entries = t_wjet.GetEntries()
# define leaves
var_tt = "MissingET.MET"
var_qcd = "MissingET.MET"
var_wjet = "MissingET.MET"
leaf_tt = t_tt.GetLeaf(var_tt)
leaf_qcd = t_qcd.GetLeaf(var_qcd)
leaf_wjet = t_wjet.GetLeaf(var_wjet)
# create the histograms
MET_tt = Hist(MET_NBINS, MET_NLO, MET_NHI, title='MET_tt', legendstyle='L')
MET_qcd = Hist(MET_NBINS,
MET_NLO,
MET_NHI,
title='MET_qcd',
legendstyle='L')
MET_wjet = Hist(MET_NBINS,
MET_NLO,
MET_NHI,
title='MET_wjet',
legendstyle='L')
# FILLING THE TREE
fill_MET_tree(tt_n_entries, t_tt, leaf_tt, MET_tt)
fill_MET_tree(qcd_n_entries, t_qcd, leaf_qcd, MET_qcd)
fill_MET_tree(wjet_n_entries, t_wjet, leaf_wjet, MET_wjet)
#set line colors
MET_tt.SetLineColor('blue')
MET_qcd.SetLineColor('green')
MET_wjet.SetLineColor('red')
#begin drawing stuff
c1 = Canvas()
MET_qcd.SetStats(0)
MET_qcd.Draw('HIST')
MET_tt.Draw('HIST SAME')
MET_wjet.Draw('HIST SAME')
#make legend
l1 = Legend([MET_tt, MET_qcd, MET_wjet], textfont=42, textsize=.03)
l1.Draw()
#save as pdf
c1.SaveAs("../plots/MET_plots/MET.pdf")
#make the plots wait on screen
wait(True)
def plotSummary(name, closure, xmin=0, xmax=1, xTitle='m_{vis} [GeV]'):
histoSmoothRatios = []
for hname, graph in closure.data['True/Est'].items():
if 'Histo' in hname and 'Smooth' in hname and not 'Error' in hname:
histoSmoothRatios.append(graph)
################ Plot ratios
histoDummyRatio = Hist(1, xmin, xmax, type='F')
values = []
for ratio in histoSmoothRatios:
for p in xrange(ratio.GetN()):
values.append(ratio.GetY()[p])
maxi = max(values)
mini = min(values)
maxi = maxi * 1.1 if maxi > 0 else maxi * 0.9
mini = mini * 1.1 if mini < 0 else mini * 0.9
histoDummyRatio.SetAxisRange(mini, maxi, 'Y')
##
for ratio in histoSmoothRatios:
ratio.SetLineColor(ROOT.kGray + 3)
ratio.SetLineWidth(1)
##
canvasRatio = Canvas(800, 800)
canvasRatio.SetName('Ratio_Summary_Canvas')
histoDummyRatio.SetXTitle(xTitle)
histoDummyRatio.SetYTitle('Ratio')
histoDummyRatio.Draw()
for ratio in histoSmoothRatios:
ratio.Draw('l same')
canvasRatio.Print(
'results/{NAME}_NonClosure_Ratio_Summary.png'.format(NAME=name))
def __init__(self, width=None, height=None,
xtitle=None, ytitle=None,
tick_length=15,
logy=False):
super(SimplePlot, self).__init__(width=width, height=height)
left, right, bottom, top = self.margin
self.SetMargin(0, 0, 0, 0)
# top pad for histograms
with self:
main = Pad(0., 0., 1., 1.)
if logy:
main.SetLogy()
main.margin = (left, right, bottom, top)
main.Draw()
# draw axes
with main:
main_hist = Hist(1, 0, 1)
main_hist.Draw('AXIS')
if xtitle is not None:
main_hist.xaxis.title = xtitle
if ytitle is not None:
main_hist.yaxis.title = ytitle
# set the tick lengths
tick_length_pixels(main, main_hist.xaxis, main_hist.yaxis,
tick_length)
self.main = main
self.main_hist = main_hist
self.logy = logy
def plotResults(variable, data, templates, results):
resCan = Canvas()
leg = Legend(nTemplates + 2)
data.Draw('PE')
leg.AddEntry(data, style='LEP')
nBins = len(inputTemplates[variable]['data'][whichBinFromFile])
h_tSumAfter = Hist(nBins, 0, nBins, title='after_' + variable)
if useT1:
plotTemplateAfter(templates[tNames['t1']], results[tNames['t1']][0],
resCan, leg, h_tSumAfter)
pass
if useT2:
plotTemplateAfter(templates[tNames['t2']], results[tNames['t2']][0],
resCan, leg, h_tSumAfter)
pass
if useT3:
plotTemplateAfter(templates[tNames['t3']], results[tNames['t3']][0],
resCan, leg, h_tSumAfter)
pass
if useT4:
plotTemplateAfter(templates[tNames['t4']], results[tNames['t4']][0],
resCan, leg, h_tSumAfter)
pass
leg.Draw()
h_tSumAfter.SetLineColor(2)
h_tSumAfter.SetLineStyle(7)
h_tSumAfter.SetMarkerSize(0)
h_tSumAfter.Draw('SAME HIST')
resCan.Update()
return resCan, h_tSumAfter
def checkOnMC(unfolding, method):
global bins, nbins
RooUnfold.SVD_n_toy = 1000
pulls = []
for sub in range(1,9):
inputFile2 = File('../data/unfolding_merged_sub%d.root' % sub, 'read')
h_data = asrootpy(inputFile2.unfoldingAnalyserElectronChannel.measured.Rebin(nbins, 'measured', bins))
nEvents = inputFile2.EventFilter.EventCounter.GetBinContent(1)
lumiweight = 164.5 * 5050 / nEvents
# print sub, nEvents
h_data.Scale(lumiweight)
doUnfoldingSequence(unfolding, h_data, method, '_sub%d' %sub)
pull = unfolding.pull_inputErrorOnly()
# unfolding.printTable()
pulls.append(pull)
unfolding.Reset()
allpulls = []
for pull in pulls:
allpulls.extend(pull)
h_allpulls = Hist(100,-30,30)
filling = h_allpulls.Fill
for entry in allpulls:
filling(entry)
fit = h_allpulls.Fit('gaus', 'WWS')
h_fit = asrootpy(h_allpulls.GetFunction("gaus").GetHistogram())
canvas = Canvas(width=1600, height=1000)
canvas.SetLeftMargin(0.15)
canvas.SetBottomMargin(0.15)
canvas.SetTopMargin(0.10)
canvas.SetRightMargin(0.05)
h_allpulls.Draw()
fit.Draw('same')
canvas.SaveAs('plots/Pull_allBins_withFit.png')
plt.figure(figsize=(16, 10), dpi=100)
rplt.errorbar(h_allpulls, label=r'Pull distribution for all bins', emptybins=False)
rplt.hist(h_fit, label=r'fit')
plt.xlabel('(unfolded-true)/error', CMS.x_axis_title)
plt.ylabel('entries', CMS.y_axis_title)
plt.title('Pull distribution for all bins', CMS.title)
plt.tick_params(**CMS.axis_label_major)
plt.tick_params(**CMS.axis_label_minor)
plt.legend(numpoints=1)
plt.savefig('plots/Pull_allBins.png')
#individual bins
for bin_i in range(nbins):
h_pull = Hist(100,-30,30)
for pull in pulls:
h_pull.Fill(pull[bin_i])
plt.figure(figsize=(16, 10), dpi=100)
rplt.errorbar(h_pull, label=r'Pull distribution for bin %d' % (bin_i + 1), emptybins=False)
plt.xlabel('(unfolded-true)/error', CMS.x_axis_title)
plt.ylabel('entries', CMS.y_axis_title)
plt.title('Pull distribution for bin %d' % (bin_i + 1), CMS.title)
plt.savefig('Pull_bin_%d.png' % (bin_i + 1))
def do_stuff(file_name, plot=True):
tiq_data = TIQData(file_name)
NFRAMES = 100
LFRAMES = 1024
tiq_data.read(nframes=NFRAMES, lframes=LFRAMES, sframes=1)
center = tiq_data.center
# do fft
ff, pp, _ = tiq_data.get_fft()
freq_lower = center + ff[0] / 1e6
freq_upper = center + ff[-1] / 1e6
# create hist
h1 = Hist(NFRAMES * LFRAMES,
freq_lower,
freq_upper,
name='h1',
title='Frequency Spectrum',
drawstyle='hist',
legendstyle='F',
fillstyle='/',
linecolor='green')
for i in range(len(pp)):
h1.set_bin_content(i, pp[i])
# set visual attributes
h1.GetXaxis().SetTitle('Freuqency [MHz]')
h1.linecolor = 'green'
# h1.fillcolor = 'green'
# h1.fillstyle = '/'
if plot:
# plot
c = Canvas(name='c1', width=700, height=500)
c.set_left_margin(0.15)
c.set_bottom_margin(0.15)
c.set_top_margin(0.10)
c.set_right_margin(0.05)
c.toggle_editor()
c.toggle_event_status()
c.toggle_tool_bar()
# c.set_crosshair()
h1.Draw()
# create the legend
legend = Legend([h1],
pad=c,
header='Header',
leftmargin=0.05,
rightmargin=0.5)
legend.Draw()
# wait for key press
wait()
def test_atlas():
style = get_style('ATLAS')
with style:
hpx = Hist(100, -4, 4, name="hpx", title="This is the px distribution")
ROOT.gRandom.SetSeed()
for i in xrange(1000):
hpx.Fill(ROOT.gRandom.Gaus())
hpx.GetXaxis().SetTitle("random variable [unit]")
hpx.GetYaxis().SetTitle("#frac{dN}{dr} [unit^{-1}]")
hpx.SetMaximum(80.)
hpx.Draw()
ATLAS_label(.4, .8)
def test_cmstdr():
style = get_style('CMSTDR')
with style:
hpx = Hist(100, -4, 4, name="hpx", title="This is the px distribution")
ROOT.gRandom.SetSeed()
for i in xrange(1000):
hpx.Fill(ROOT.gRandom.Gaus())
hpx.GetXaxis().SetTitle("random variable [unit]")
hpx.GetYaxis().SetTitle("#frac{dN}{dr} [unit^{-1}]")
hpx.SetMaximum(100.)
hpx.Draw()
labels.CMS_label("Testing 2050", sqrts=100)
def test_lhcb():
style = get_style('LHCb')
with style:
canvas = Canvas()
hpx = Hist(100, -4, 4, name="hpx", title="This is the px distribution")
ROOT.gRandom.SetSeed()
for i in xrange(1000):
hpx.Fill(ROOT.gRandom.Gaus())
hpx.GetXaxis().SetTitle("random variable [unit]")
hpx.GetYaxis().SetTitle("#frac{dN}{dr} [unit^{-1}]")
hpx.SetMaximum(80.)
hpx.Draw()
LHCb_label("R", "preliminary")
if INTERACTIVE:
wait()
def draw_efficiencies(effs, field, category, textsize=22):
xtitle = get_xtitle(field)
c = Canvas()
c.SetGridx()
c.SetGridy()
if not isinstance(effs, (list, tuple)):
effs = [effs]
h = Hist(10, effs[0].painted_graph.xaxis.min,
effs[0].painted_graph.xaxis.max)
h.yaxis.SetRangeUser(0.0, 1.1)
h.Draw('HIST')
h.yaxis.title = 'Efficiency'
h.xaxis.title = xtitle
colors = [
'black',
'red',
'orange',
'blue',
'green',
'purple',
'yellow',
'pink',
]
if len(effs) > len(colors):
colors = len(effs) * colors
for eff, col in zip(effs, colors):
eff.color = col
eff.color = col
eff.painted_graph.legendstyle = 'l'
eff.painted_graph.Draw('SAMEP')
label = ROOT.TLatex(c.GetLeftMargin() + 0.04, 0.9, category.label)
label.SetNDC()
label.SetTextFont(43)
label.SetTextSize(textsize)
label.Draw()
leg = Legend(effs, pad=c, textsize=20)
# textsize=20, leftmargin=0.6, topmargin=0.6)
leg.Draw('same')
return c
def plotDataMC(name, closureData, closureMC, bins, xTitle='m_{vis} [GeV]'):
binid = hash(str(bins))
#
histoRatioData = closureData.data['True/Est']['Histo_{H}'.format(H=binid)]
histoSmoothRatioData = closureData.data['True/Est'][
'Histo_{H}_Smooth'.format(H=binid)]
histoSmoothRatioErrorData = closureData.data['True/Est'][
'Histo_{H}_SmoothError'.format(H=binid)]
histoRatioMC = closureMC.data['True/Est']['Histo_{H}'.format(H=binid)]
histoSmoothRatioMC = closureMC.data['True/Est']['Histo_{H}_Smooth'.format(
H=binid)]
histoSmoothRatioErrorMC = closureMC.data['True/Est'][
'Histo_{H}_SmoothError'.format(H=binid)]
#
################ Plot ratios
histoDummyRatio = Hist(1, bins[0], bins[-1], type='F')
values = []
values.append(histoRatioData.GetMaximum())
values.append(histoRatioData.GetMinimum())
values.append(histoRatioMC.GetMaximum())
values.append(histoRatioMC.GetMinimum())
maxi = max(values)
mini = min(values)
maxi = maxi * 1.1 if maxi > 0 else maxi * 0.9
mini = mini * 1.1 if mini < 0 else mini * 0.9
histoDummyRatio.SetAxisRange(mini, maxi, 'Y')
##
histoRatioData.SetMarkerColor(ROOT.kBlack)
histoSmoothRatioData.SetLineColor(ROOT.kGray + 3)
histoSmoothRatioData.SetLineWidth(2)
histoSmoothRatioErrorData.SetLineColor(ROOT.kGray + 1)
histoSmoothRatioErrorData.SetFillColor(ROOT.kGray + 1)
##
histoRatioMC.SetMarkerColor(ROOT.kRed)
histoRatioMC.SetMarkerStyle(21)
histoSmoothRatioMC.SetLineColor(ROOT.kRed)
histoSmoothRatioMC.SetLineWidth(2)
histoSmoothRatioErrorMC.SetLineColor(ROOT.kRed - 7)
#histoSmoothRatioErrorMC.SetFillColorAlpha(ROOT.kRed-7, 0.35)
histoSmoothRatioErrorMC.SetFillStyle(3344)
histoSmoothRatioErrorMC.SetFillColor(ROOT.kRed - 7)
##
canvasRatio = Canvas(800, 800)
canvasRatio.SetName('{NAME}_Ratio_Canvas'.format(NAME=name))
histoDummyRatio.SetXTitle(xTitle)
histoDummyRatio.SetYTitle('Ratio')
histoDummyRatio.Draw()
histoSmoothRatioErrorData.Draw('fl same')
histoSmoothRatioData.Draw('pl same')
histoSmoothRatioErrorMC.Draw('fl same')
histoSmoothRatioMC.Draw('pl same')
histoRatioData.Draw('E1 same')
histoRatioMC.Draw('E1 same')
#
legend = ROOT.TLegend(0.15, 0.75, 0.45, 0.9)
legend.SetFillColor(0)
legend.SetLineColor(0)
legend.AddEntry(histoRatioMC, 'MC', 'lp')
legend.AddEntry(histoRatioData, 'Data', 'lp')
legend.Draw()
canvasRatio.Print('results/{NAME}_NonClosure_Ratio.png'.format(NAME=name))
def __init__(self,
width=None,
height=None,
offset=0,
ratio_height=None,
ratio_margin=26,
ratio_limits=(0, 2),
ratio_divisions=4,
prune_ratio_ticks=False,
ratio_line_values=(1, ),
ratio_line_width=2,
ratio_line_style='dashed',
xtitle=None,
ytitle=None,
ratio_title=None,
tick_length=15,
logy=False):
# first init as normal canvas
super(RatioPlot, self).__init__(width=width, height=height)
# get margins in pixels
left, right, bottom, top = self.margin_pixels
default_height = self.height
default_frame_height = default_height - bottom - top
if ratio_height is None:
ratio_height = default_height / 4.
self.height += int(ratio_height) + ratio_margin + offset
self.margin = (0, 0, 0, 0)
main_height = default_frame_height + top + ratio_margin / 2. + offset
ratio_height += ratio_margin / 2. + bottom
# top pad for histograms
with self:
main = Pad(0., ratio_height / self.height, 1., 1.)
if logy:
main.SetLogy()
main.margin_pixels = (left, right, ratio_margin / 2., top)
main.Draw()
# bottom pad for ratio plot
with self:
ratio = Pad(0, 0, 1, ratio_height / self.height)
ratio.margin_pixels = (left, right, bottom, ratio_margin / 2.)
ratio.Draw()
# draw main axes
with main:
main_hist = Hist(1, 0, 1)
main_hist.Draw('AXIS')
# hide x-axis labels and title on main pad
xaxis, yaxis = main_hist.xaxis, main_hist.yaxis
xaxis.SetLabelOffset(1000)
xaxis.SetTitleOffset(1000)
# adjust y-axis title spacing
yaxis.SetTitleOffset(yaxis.GetTitleOffset() * self.height /
default_height)
# draw ratio axes
with ratio:
ratio_hist = Hist(1, 0, 1)
ratio_hist.Draw('AXIS')
# adjust x-axis label and title spacing
xaxis, yaxis = ratio_hist.xaxis, ratio_hist.yaxis
xaxis.SetLabelOffset(xaxis.GetLabelOffset() * self.height /
ratio_height)
xaxis.SetTitleOffset(xaxis.GetTitleOffset() * self.height /
ratio_height)
# adjust y-axis title spacing
yaxis.SetTitleOffset(yaxis.GetTitleOffset() * self.height /
default_height)
if ratio_limits is not None:
low, high = ratio_limits
if prune_ratio_ticks:
delta = 0.01 * (high - low) / float(ratio_divisions % 100)
low += delta
high -= delta
yaxis.SetLimits(low, high)
yaxis.SetRangeUser(low, high)
yaxis.SetNdivisions(ratio_divisions)
if xtitle is not None:
ratio_hist.xaxis.title = xtitle
if ytitle is not None:
main_hist.yaxis.title = ytitle
if ratio_title is not None:
ratio_hist.yaxis.title = ratio_title
# set the tick lengths
tick_length_pixels(main, main_hist.xaxis, main_hist.yaxis, tick_length)
tick_length_pixels(ratio, ratio_hist.xaxis, ratio_hist.yaxis,
tick_length)
# draw ratio lines
lines = []
if ratio_line_values:
with ratio:
for value in ratio_line_values:
line = Line(0, value, 1, value)
line.linestyle = ratio_line_style
line.linewidth = ratio_line_width
line.Draw()
lines.append(line)
self.lines = lines
self.main = main
self.main_hist = main_hist
self.ratio = ratio
self.ratio_hist = ratio_hist
self.ratio_limits = ratio_limits
self.logy = logy
legendstyle='l')
h2 = Hist(100,
-5,
5,
name="h2",
title="Histogram 2",
linecolor='blue',
legendstyle='l')
for ievt in range(10000):
h1.Fill(rand.Gaus(0, 0.8))
h2.Fill(rand.Gaus(0, 1))
pad = c.cd(1)
h1.Draw('hist')
h2.Draw('hist same')
leg = Legend([h1, h2],
pad=pad,
leftmargin=0.5,
topmargin=0.11,
rightmargin=0.05,
textsize=20)
leg.Draw()
pad = c.cd(2)
gr = qqgraph(h1, h2)
gr.xaxis.title = h1.title
total_vs_pu).decorate(linecolor='green',
linewidth=2,
markerstyle=20,
markercolor='green')
frame = Hist(*binning)
frame.SetMaximum(1)
frame.SetMinimum(0)
frame.axis().SetTitle("Number of vertices")
frame.axis(2).SetTitle("Efficiency (w.r.t. reco. p_{T}>40 GeV)")
canvas.SetLogy(False)
canvas.SetLeftMargin(0.2)
frame.Draw()
l1_efficiency.Draw('pe same')
uct_efficiency.Draw('pe same')
uct_iso_efficiency.Draw('pe same')
legend = Legend(3, topmargin=0.25, leftmargin=0.25)
legend.AddEntry(l1_efficiency, 'lp', 'Current Tau44')
legend.AddEntry(uct_efficiency, 'lp',
'Upgrade RlxTau25 Rel. Rate %0.2f' % (9488. / 9355))
legend.AddEntry(uct_iso_efficiency, 'lp',
'Upgrade IsoTau25 Rel. Rate %0.2f' % (6224. / 9355))
legend.SetBorderSize(0)
legend.SetTextSize(0.03)
legend.Draw()
i, modules_hist[j].GetBinContent(i) / args.bw)
modules_hist[j].SetBinError(i,
modules_hist[j].GetBinError(i) / args.bw)
print ' - drawing ... '
y_max = 0
for i in xrange(25):
if modules_hist[i].GetMaximum() > y_max:
y_max = modules_hist[i].GetMaximum()
for i in xrange(25):
modules_hist[i].SetMaximum(y_max * 1.1)
canvas_trigger = Canvas(1000,
800,
name="canvas_trigger",
title="rate of event trigger")
canvas_trigger.ToggleEventStatus()
canvas_trigger.cd()
trigger_hist.Draw('EH')
canvas_modules = Canvas(1500,
1000,
name="canvas_modules",
title="rate of 25 modules")
canvas_modules.ToggleEventStatus()
canvas_modules.Divide(5, 5)
for i in xrange(25):
canvas_modules.cd(5 * (i % 5) + i / 5 + 1)
modules_hist[i].Draw('EH')
sci_trigger_r_obj.close_file()
wait(True)
ROOT.gStyle.SetNumberContours(255)
#mask.fillstyle = 3354
#mask.fillcolor = 1
#mask.zaxis.range_user = (-0.9, 0.9)
hdrawopts = 'HIST text' if args.conly else 'colz text'
fitdrawopts = '' if args.conly else 'P SAME'
if not args.conly:
lbias.Draw(hdrawopts)
fitted.Draw(fitdrawopts)
canvas.SaveAs('%s/lbias.png' % indir)
canvas.SaveAs('%s/lbias.pdf' % indir)
cbias.Draw(hdrawopts)
if args.conly:
fitted.SetY1(-0.13)
fitted.SetY2(-0.19)
fitted.Draw(fitdrawopts)
canvas.SaveAs('%s/cbias.png' % indir)
canvas.SaveAs('%s/cbias.pdf' % indir)
ROOT.TColor.CreateGradientColorTable(2, array('d', [0., 1.]),
array('d',
[0., 1.]), array('d', [0., 0.]),
array('d', [1., 0.]), 256)
failing.Draw(hdrawopts) #'HIST' if args.conly else 'colz')
getattr(failing, axis).range_user = (-0.1, 1)
if args.conly:
fitted.SetY1(0.075)
linewidth=2)
up.Reset()
up.fill_array(transform(up_scores))
dn = nominal.Clone(title='Down',
linecolor='blue',
linestyle='dashed',
linewidth=2)
dn.Reset()
dn.fill_array(transform(dn_scores))
# Plot the nominal, up, and down scores
canvas = Canvas()
nominal.SetMaximum(max(dn) * 1.1)
nominal.Draw()
nominal.xaxis.SetTitle('BDT Score')
nominal.yaxis.SetTitle('Events')
up.Draw('same hist')
dn.Draw('same hist')
leg = Legend(3, pad=canvas, leftmargin=.5)
leg.AddEntry(nominal, style='LEP')
leg.AddEntry(up, style='L')
leg.AddEntry(dn, style='L')
leg.Draw()
canvas.SaveAs('canvas_original.png')
# Take the ratio of systematic / nominal
def extract_Electron(f_tt, f_qcd, f_wjet):
# get trees from files
t_tt = f_tt.Get("Delphes")
t_qcd = f_qcd.Get("Delphes")
t_wjet = f_wjet.Get("Delphes")
# get number of entries
tt_n_entries = t_tt.GetEntries()
qcd_n_entries = t_qcd.GetEntries()
wjet_n_entries = t_wjet.GetEntries()
# define leaves
var_tt = "Electron.PT"
var_qcd = "Electron.PT"
var_wjet = "Electron.PT"
leaf_tt = t_tt.GetLeaf(var_tt)
leaf_qcd = t_qcd.GetLeaf(var_qcd)
leaf_wjet = t_wjet.GetLeaf(var_wjet)
# create the histograms
numElectrons_tt = Hist(NBINS,NLO,NHI, title = 'numElectrons_tt', legendstyle = 'L')
numElectrons_qcd = Hist(NBINS,NLO,NHI, title = 'numElectrons_qcd', legendstyle = 'L')
numElectrons_wjet = Hist(NBINS,NLO,NHI, title = 'numElectrons_wjet', legendstyle = 'L')
# interesting values to plot
max_ept_per_event_tt = Hist(PT_NBINS,PT_NLO,PT_NHI, title = 'Max ElectronPT/Event tt', legendstyle = 'L')
min_ept_per_event_tt = Hist(PT_NBINS,PT_NLO,PT_NHI, title = 'Min ElectronPT/Event tt', legendstyle = 'L')
max_ept_per_event_qcd = Hist(PT_NBINS,PT_NLO,PT_NHI, title = 'Max ElectronPT/Event qcd', legendstyle = 'L')
min_ept_per_event_qcd = Hist(PT_NBINS,PT_NLO,PT_NHI, title = 'Min ElectronPT/Event qcd', legendstyle = 'L')
max_ept_per_event_wjet = Hist(PT_NBINS,PT_NLO,PT_NHI, title = 'Max ElectronPT/Event wjet', legendstyle = 'L')
min_ept_per_event_wjet = Hist(PT_NBINS,PT_NLO,PT_NHI, title = 'Min ElectronPT/Event wjet', legendstyle = 'L')
# FILLING THE TREE
fill_Electron_tree(tt_n_entries, t_tt, leaf_tt, numElectrons_tt, min_ept_per_event_tt, max_ept_per_event_tt)
fill_Electron_tree(qcd_n_entries, t_qcd, leaf_qcd, numElectrons_qcd, min_ept_per_event_qcd, max_ept_per_event_qcd)
fill_Electron_tree(wjet_n_entries, t_wjet, leaf_wjet, numElectrons_wjet, min_ept_per_event_wjet, max_ept_per_event_wjet)
#set line colors
numElectrons_tt.SetLineColor('blue')
numElectrons_qcd.SetLineColor('green')
numElectrons_wjet.SetLineColor('red')
#begin drawing stuff
c1 = Canvas()
numElectrons_wjet.SetStats(0)
numElectrons_wjet.Draw('HIST')
numElectrons_tt.Draw('HIST SAME')
numElectrons_qcd.Draw('HIST SAME')
#make legend
l1 = Legend([numElectrons_tt, numElectrons_qcd, numElectrons_wjet], textfont = 42, textsize = .03)
l1.Draw()
#save as pdf
c1.SaveAs("../plots/ElectronPT_plots/numElectrons.pdf");
################ MIN MAX STUFF
# TT
#set line colors
max_ept_per_event_tt.SetLineColor('blue')
min_ept_per_event_tt.SetLineColor('green')
#begin drawing stuff
c2 = Canvas()
min_ept_per_event_tt.SetStats(0)
min_ept_per_event_tt.Draw('HIST')
max_ept_per_event_tt.Draw('HIST SAME')
#make legend
l2 = Legend([min_ept_per_event_tt, max_ept_per_event_tt], textfont = 42, textsize = .03)
l2.Draw()
#save as pdf
c2.SaveAs("../plots/ElectronPT_plots/e_maxminpt_tt.pdf")
# QCD
#set line colors
max_ept_per_event_qcd.SetLineColor('blue')
min_ept_per_event_qcd.SetLineColor('green')
#begin drawing stuff
c3 = Canvas()
max_ept_per_event_qcd.SetStats(0)
max_ept_per_event_qcd.Draw('HIST')
min_ept_per_event_qcd.Draw('HIST SAME')
#make legend
l3 = Legend([min_ept_per_event_qcd, max_ept_per_event_qcd], textfont = 42, textsize = .03)
l3.Draw()
#save as pdf
c3.SaveAs("../plots/ElectronPT_plots/e_maxminpt_qcd.pdf")
#WJET
#set line colors
max_ept_per_event_wjet.SetLineColor('blue')
min_ept_per_event_wjet.SetLineColor('green')
#begin drawing stuff
c4 = Canvas()
min_ept_per_event_wjet.SetStats(0)
min_ept_per_event_wjet.Draw('HIST')
max_ept_per_event_wjet.Draw('HIST SAME')
#make legend
l4 = Legend([min_ept_per_event_wjet, max_ept_per_event_wjet], textfont = 42, textsize = .03)
l4.Draw()
#save as pdf
c4.SaveAs("../plots/ElectronPT_plots/e_maxminpt_wjet.pdf")
#make the plots wait on screen
wait(True)
sys = nom.Clone(linewidth=2, linestyle='dashed')
nom.Smooth(10)
for i, bin in enumerate(sys.bins()):
bin.value += i / 10.
smooth_sys = smooth(nom,
sys,
10,
linecolor='red',
linewidth=2,
linestyle='dashed')
smooth_alt_sys = smooth_alt(nom,
sys,
linecolor='blue',
linewidth=2,
linestyle='dashed')
nom.SetMaximum(
max(nom.GetMaximum(), sys.GetMaximum(), smooth_sys.GetMaximum(),
smooth_alt_sys.GetMaximum()) * 1.2)
nom.SetMinimum(-1)
nom.Draw('hist')
sys.Draw('hist same')
smooth_sys.Draw('hist same')
smooth_alt_sys.Draw('hist same')
wait()
def pvalue_plot(poi,
pvalues,
pad=None,
xtitle='X',
ytitle='P_{0}',
linestyle=None,
linecolor=None,
yrange=None,
verbose=False):
"""
Draw a pvalue plot
Parameters
----------
poi : list
List of POI values tested
pvalues : list
List of p-values or list of lists of p-values to overlay
multiple p-value curves
pad : Canvas or Pad, optional (default=None)
Pad to draw onto. Create new pad if None.
xtitle : str, optional (default='X')
The x-axis label (POI name)
ytitle : str, optional (default='P_{0}')
The y-axis label
linestyle : str or list, optional (default=None)
Line style for the p-value graph or a list of linestyles for
multiple p-value graphs.
linecolor : str or list, optional (default=None)
Line color for the p-value graph or a list of linestyles for
multiple p-value graphs.
Returns
-------
pad : Canvas
The pad.
graphs : list of Graph
The p-value graphs
"""
if not pvalues:
raise ValueError("pvalues is empty")
if not poi:
raise ValueError("poi is empty")
# determine if pvalues is list or list of lists
if not isinstance(pvalues[0], (list, tuple)):
pvalues = [pvalues]
if linecolor is not None:
if not isinstance(linecolor, list):
linecolor = [linecolor]
linecolor = cycle(linecolor)
if linestyle is not None:
if not isinstance(linestyle, list):
linestyle = [linestyle]
linestyle = cycle(linestyle)
with preserve_current_canvas():
if pad is None:
pad = Canvas()
pad.cd()
pad.SetLogy()
# create the axis
min_poi, max_poi = min(poi), max(poi)
haxis = Hist(1000, min_poi, max_poi)
xaxis = haxis.xaxis
yaxis = haxis.yaxis
xaxis.SetRangeUser(min_poi, max_poi)
haxis.Draw('AXIS')
min_pvalue = float('inf')
graphs = []
for ipv, pv in enumerate(pvalues):
graph = Graph(len(poi),
linestyle='dashed',
drawstyle='L',
linewidth=2)
for idx, (point, pvalue) in enumerate(zip(poi, pv)):
graph.SetPoint(idx, point, pvalue)
if linestyle is not None:
graph.linestyle = linestyle.next()
if linecolor is not None:
graph.linecolor = linecolor.next()
graphs.append(graph)
curr_min_pvalue = min(pv)
if curr_min_pvalue < min_pvalue:
min_pvalue = curr_min_pvalue
if verbose:
for graph in graphs:
log.info(['{0:1.1f}'.format(xval) for xval in list(graph.x())])
log.info(['{0:0.3f}'.format(yval) for yval in list(graph.y())])
# automatically handles axis limits
axes, bounds = draw(graphs,
pad=pad,
same=True,
logy=True,
xtitle=xtitle,
ytitle=ytitle,
xaxis=xaxis,
yaxis=yaxis,
ypadding=(0.2, 0.1),
logy_crop_value=1E-300)
if yrange is not None:
xaxis, yaxis = axes
yaxis.SetLimits(*yrange)
yaxis.SetRangeUser(*yrange)
min_pvalue = yrange[0]
# draw sigma levels up to minimum of pvalues
line = Line()
line.SetLineStyle(2)
line.SetLineColor(2)
latex = ROOT.TLatex()
latex.SetNDC(False)
latex.SetTextSize(20)
latex.SetTextColor(2)
sigma = 0
while True:
pvalue = gaussian_cdf_c(sigma)
if pvalue < min_pvalue:
break
keepalive(
pad,
latex.DrawLatex(max_poi, pvalue, " {0}#sigma".format(sigma)))
keepalive(pad, line.DrawLine(min_poi, pvalue, max_poi, pvalue))
sigma += 1
pad.RedrawAxis()
pad.Update()
return pad, graphs
map(h2.Fill, x2)
# normalize the histograms
h1 /= h1.Integral()
h2 /= h2.Integral()
# set visual attributes
h1.SetFillStyle("solid")
h1.SetFillColor("green")
h1.SetLineColor("green")
h2.SetFillStyle("solid")
h2.SetFillColor("red")
h2.SetLineColor("red")
# plot with ROOT
h1.GetXaxis().SetTitle('Smarts')
h1.GetYaxis().SetTitle('Probability')
h1.SetTitle("Histogram of IQ: #mu=100, #sigma=15")
h1.Draw("hist")
h2.Draw("same")
# plot with matplotlib
plt.figure()
rplt.hist(h1, alpha=0.75)
rplt.hist(h2, alpha=0.75)
plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title(r'$\mathrm{Histogram\ of\ IQ:}\ \mu=100,\ \sigma=15$')
plt.show()
map(h.Fill, x)
# normalize the histogram
h /= h.Integral()
# set visual attributes
h.SetFillStyle('/')
h.SetFillColor('green')
h.SetLineColor('green')
# plot with ROOT
c = Canvas(width=800, height=600)
h.GetXaxis().SetTitle('Smarts')
h.GetYaxis().SetTitle('Probability')
h.SetTitle("Histogram of IQ: #mu=100, #sigma=15")
h.Draw("hist")
legend = Legend(1)
legend.AddEntry(h, 'F')
legend.Draw()
c.SaveAs('root_hist.png')
# plot with matplotlib
plt.figure(figsize=(8, 6), dpi=100)
rplt.hist(h, label=r'$\mathrm{Histogram\ of\ IQ:}\ \mu=100,\ \sigma=15$', alpha=0.75)
plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title(r'$\mathrm{Histogram\ of\ IQ:}\ \mu=100,\ \sigma=15$')
plt.legend()
plt.savefig('matplotlib_hist.png')
plt.show()
from rootpy.extern.six.moves import range
try:
kwargs = {}
for arg in extra:
name, value = arg.lstrip('--').split('=')
kwargs[name] = value
except ValueError:
print("specify style parameters with --name=value")
try:
style = get_style(args.style, **kwargs)
except ValueError:
print('Invalid style: `{0}`. Using the `ATLAS` style.'.format(args.style))
style = get_style('ATLAS')
# Use styles as context managers. The selected style will only apply
# within the following context:
with style:
c = Canvas()
hpx = Hist(100, -4, 4, name="hpx", title="This is the px distribution")
# generate some random data
ROOT.gRandom.SetSeed()
for i in range(25000):
hpx.Fill(ROOT.gRandom.Gaus())
hpx.GetXaxis().SetTitle("random variable [unit]")
hpx.GetYaxis().SetTitle("#frac{dN}{dr} [unit^{-1}]")
hpx.SetMaximum(1000.)
hpx.Draw()
wait()
h_data_toUse.SetBinContent(bin, newContent)
h_data_toUse.SetBinError(bin, sqrt(newContent))
h_measured_toUse = asrootpy(h_measured)
# Remove fakes before unfolding
h_fakes = h_measured_toUse - h_response.ProjectionX()
nonFakeRatio = 1 - h_fakes / h_measured_toUse
h_measured_toUse *= nonFakeRatio / nonFakeRatio
h_data_toUse *= nonFakeRatio / nonFakeRatio
# Unfold with SVD
response = RooUnfoldResponse(h_measured_toUse, h_truth, h_response)
svdUnfolding = RooUnfoldSvd(response, h_data_toUse, 0.7)
svdUnfolding.SetVerbose(0)
h_unfolded_data_svd = svdUnfolding.Hreco(3)
# Unfold with Bayes
response_bayes = RooUnfoldResponse(h_measured_toUse, h_truth, h_response)
bayesUnfolding = RooUnfoldBayes(response_bayes, h_data_toUse, 4)
h_unfolded_data_bayes = bayesUnfolding.Hreco(3)
# Store result of first bin
h_svdFirstBin.Fill(h_unfolded_data_svd.GetBinContent(1))
h_bayesFirstBin.Fill(h_unfolded_data_bayes.GetBinContent(1))
# Draw histograms
h_svdFirstBin.Draw()
raw_input('SVD')
h_bayesFirstBin.Draw()
raw_input('Bayes')
else:
axis = 0
# randomly choose starting bin
start = randint(1, h.nbins(axis) - 1)
end = randint(start + 1, min(start + 5 * h.GetDimension(), h.nbins(axis)))
return h.merge_bins([(start, end)], axis=axis)
# create an animation of a 1D histogram
c1 = Canvas()
if not BATCH and os.path.isfile('binmerge1d.gif'):
os.unlink('binmerge1d.gif')
a = Hist(100, -5, 5)
a.fill_array(np.random.randn(10000))
while a.nbins(0) > 1:
a = random_bin_merge(a)
a.Draw('hist')
if not BATCH:
c1.Print('binmerge1d.gif+20')
time.sleep(.1)
if not BATCH:
c1.Print('binmerge1d.gif++')
# create an animation of a 2D histogram
c2 = Canvas()
if not BATCH and os.path.isfile('binmerge2d.gif'):
os.unlink('binmerge2d.gif')
c2.SetRightMargin(0.1)
b = Hist2D(100, -5, 5, 100, -5, 5)
b.fill_array(np.random.randn(10000, 2))
while b.nbins(0) > 1 or b.nbins(1) > 1:
b = random_bin_merge(b)
h_t4.SetLineColor(7)
h_data.SetLineColor(1)
h_data.SetMarkerColor(1)
ymax = getMax([h_data, h_t1, h_t2, h_t3])
ymax = ymax * 1.1
h_data.GetYaxis().SetRangeUser(0, ymax)
h_t1.GetYaxis().SetRangeUser(0, ymax)
h_t2.GetYaxis().SetRangeUser(0, ymax)
h_t3.GetYaxis().SetRangeUser(0, ymax)
h_t4.GetYaxis().SetRangeUser(0, ymax)
c = Canvas()
c.Divide(2)
c.cd(1)
h_data.Draw('PE')
h_t1.Draw('SAME HIST')
h_t2.Draw('SAME HIST')
h_t3.Draw('SAME HIST')
h_t4.Draw('SAME HIST')
templates = {}
if useT1: templates['t1'] = h_t1
if useT2: templates['t2'] = h_t2
if useT3: templates['t3'] = h_t3
if useT4: templates['t4'] = h_t4
fit_data = FitData(h_data, templates, fit_boundaries=(0, h_data.nbins()))
fit_collection = FitDataCollection()
fit_collection.add(fit_data)
h_simple.linecolor = 'blue'
h_simple.fillcolor = 'green'
h_simple.fillstyle = '/'
# attributes may be accessed in the same way
print(h_simple.name)
print(h_simple.title)
print(h_simple.markersize)
# plot
canvas = Canvas(width=700, height=500)
canvas.SetLeftMargin(0.15)
canvas.SetBottomMargin(0.15)
canvas.SetTopMargin(0.10)
canvas.SetRightMargin(0.05)
h_simple.Draw()
# create the legend
legend = Legend([h_simple],
pad=canvas,
header='Header',
leftmargin=0.05,
rightmargin=0.5)
legend.Draw()
# 2D and 3D histograms are handled in the same way
# the constructor arguments are repetitions of #bins, left bound, right bound.
h2d = Hist2D(10, 0, 1, 50, -40, 10, name='2d hist')
h3d = Hist3D(3, -1, 4, 10, -1000, -200, 2, 0, 1, name='3d hist')
# variable-width bins may be created by passing the bin edges directly:
def mpe_fitting(filename, run, num_photons, use_ideal=True):
run_number = int(run)
if filename[-5:] == '.root':
filename = filename[:-5]
s_data_path = './data/%s.p' % filename
if filename[:5] == 'nerix':
file_identifier = filename
else:
file_identifier = filename[-9:]
a_integral = pickle.load((open(s_data_path, 'r')))
# max_num_events used to limit amplitudes
max_num_events = len(a_integral)
s_path_to_save = './results/%s/' % (file_identifier)
l_colors = [4, 2, 8, 7, 5, 9] + [4, 2, 8, 7, 5, 9]
d_mpe_fit = {}
if file_identifier == '0062_0061':
d_mpe_fit['settings'] = [250, -1e6, 2e7]
d_mpe_fit['bkg_mean_low'] = -1e6
d_mpe_fit['bkg_mean_high'] = 2e6
d_mpe_fit['bkg_width_low'] = 1e4
d_mpe_fit['bkg_width_high'] = 1e6
d_mpe_fit['spe_mean_low'] = 3.5e6
d_mpe_fit['spe_mean_high'] = 1e7
d_mpe_fit['spe_width_low'] = 8e5
d_mpe_fit['spe_width_high'] = 3e6
d_mpe_fit['ua_mean_low'] = 1e5
d_mpe_fit['ua_mean_high'] = 5e6
d_mpe_fit['ua_width_low'] = 1e4
d_mpe_fit['ua_width_high'] = 1e6
d_mpe_fit['spe_mean_guess'] = 6e6
d_mpe_fit['spe_width_guess'] = 1.9e6
elif file_identifier == '0066_0065':
d_mpe_fit['settings'] = [250, -1e6, 1.2e7]
d_mpe_fit['bkg_mean_low'] = -1e6
d_mpe_fit['bkg_mean_high'] = 2e6
d_mpe_fit['bkg_width_low'] = 1e4
d_mpe_fit['bkg_width_high'] = 1e6
d_mpe_fit['spe_mean_low'] = 2e6
d_mpe_fit['spe_mean_high'] = 6e6
d_mpe_fit['spe_width_low'] = 8e5
d_mpe_fit['spe_width_high'] = 3e6
d_mpe_fit['ua_mean_low'] = 1e5
d_mpe_fit['ua_mean_high'] = 5e6
d_mpe_fit['ua_width_low'] = 1e4
d_mpe_fit['ua_width_high'] = 1e6
d_mpe_fit['spe_mean_guess'] = 3.6e6
d_mpe_fit['spe_width_guess'] = 1.1e6
elif file_identifier == '0067_0068':
d_mpe_fit['settings'] = [250, -1e6, 7.5e6]
d_mpe_fit['bkg_mean_low'] = -5e5
d_mpe_fit['bkg_mean_high'] = 5e5
d_mpe_fit['bkg_width_low'] = 1e4
d_mpe_fit['bkg_width_high'] = 1e6
d_mpe_fit['spe_mean_low'] = 1.2e6
d_mpe_fit['spe_mean_high'] = 3.5e6
d_mpe_fit['spe_width_low'] = 3e5
d_mpe_fit['spe_width_high'] = 1e6
d_mpe_fit['ua_mean_low'] = 1e5
d_mpe_fit['ua_mean_high'] = 1.5e6
d_mpe_fit['ua_width_low'] = 1e4
d_mpe_fit['ua_width_high'] = 1e6
d_mpe_fit['spe_mean_guess'] = 2.1e6
d_mpe_fit['spe_width_guess'] = 6.6e5
elif file_identifier == '0071_0072':
d_mpe_fit['settings'] = [250, -1e6, 3.4e7]
d_mpe_fit['bkg_mean_low'] = -1e6
d_mpe_fit['bkg_mean_high'] = 2e6
d_mpe_fit['bkg_width_low'] = 1e4
d_mpe_fit['bkg_width_high'] = 1e6
d_mpe_fit['spe_mean_low'] = 7.5e6
d_mpe_fit['spe_mean_high'] = 1.4e7
d_mpe_fit['spe_width_low'] = 1e6
d_mpe_fit['spe_width_high'] = 3.5e6
d_mpe_fit['ua_mean_low'] = 1e5
d_mpe_fit['ua_mean_high'] = 5e6
d_mpe_fit['ua_width_low'] = 1e4
d_mpe_fit['ua_width_high'] = 1e6
d_mpe_fit['spe_mean_guess'] = 9.5e6
d_mpe_fit['spe_width_guess'] = 2.9e6
elif file_identifier == '0073_0074':
d_mpe_fit['settings'] = [50, -1e6, 4.2e7]
d_mpe_fit['bkg_mean_low'] = -1e6
d_mpe_fit['bkg_mean_high'] = 2e6
d_mpe_fit['bkg_width_low'] = 1e5
d_mpe_fit['bkg_width_high'] = 2e6
d_mpe_fit['spe_mean_low'] = 8.5e6
d_mpe_fit['spe_mean_high'] = 1.4e7
d_mpe_fit['spe_width_low'] = 1.5e6
d_mpe_fit['spe_width_high'] = 4.5e6
d_mpe_fit['ua_mean_low'] = 5e5
d_mpe_fit['ua_mean_high'] = 4e6
d_mpe_fit['ua_width_low'] = 0.5e6
d_mpe_fit['ua_width_high'] = 1e6
d_mpe_fit['spe_mean_guess'] = 9.5e6
d_mpe_fit['spe_width_guess'] = 2.9e6
elif file_identifier == 'nerix_160418_1523':
d_mpe_fit['settings'] = [50, -5e5, 3.e6]
d_mpe_fit['bkg_mean_low'] = -5e5
d_mpe_fit['bkg_mean_high'] = 5e5
d_mpe_fit['bkg_width_low'] = 1e4
d_mpe_fit['bkg_width_high'] = 5e5
d_mpe_fit['spe_mean_low'] = 6e5
d_mpe_fit['spe_mean_high'] = 11e5
d_mpe_fit['spe_width_low'] = 3e5
d_mpe_fit['spe_width_high'] = 9e5
d_mpe_fit['ua_mean_low'] = 1e3
d_mpe_fit['ua_mean_high'] = 5e5
d_mpe_fit['ua_width_low'] = 1e4
d_mpe_fit['ua_width_high'] = 5e5
elif file_identifier == 'nerix_160418_1531':
d_mpe_fit['settings'] = [50, -5e5, 4.e6]
d_mpe_fit['bkg_mean_low'] = -5e5
d_mpe_fit['bkg_mean_high'] = 5e5
d_mpe_fit['bkg_width_low'] = 1e4
d_mpe_fit['bkg_width_high'] = 5e5
d_mpe_fit['spe_mean_low'] = 6e5
d_mpe_fit['spe_mean_high'] = 11e5
d_mpe_fit['spe_width_low'] = 3e5
d_mpe_fit['spe_width_high'] = 9e5
d_mpe_fit['ua_mean_low'] = 1e3
d_mpe_fit['ua_mean_high'] = 5e5
d_mpe_fit['ua_width_low'] = 1e4
d_mpe_fit['ua_width_high'] = 5e5
else:
print '\n\nSettings do not exist for given setup: %s\n\n' % (file_identifier)
sys.exit()
l_plots = ['plots', file_identifier]
par_names = ['p0_ampl', 'mean_bkg', 'width_bkg', 'mean_spe', 'width_spe'] + ['p%d_ampl' % (i + 1) for i in xrange(num_photons)]
a_integral = pickle.load((open(s_data_path, 'r')))
if use_ideal:
l_mpe_fit_func = ['[5]/(2*3.14*%d*[4]**2.)**0.5*TMath::Poisson(%d, [0])*exp(-0.5/%.1f*((x - %.1f*[3])/[4])**2)' % (iElectron, iElectron, iElectron, iElectron) for iElectron in xrange(1, num_photons + 1)]
else:
l_mpe_fit_func = ['[5]/(2*3.14*([2]**2. + %d*[4]**2.))**0.5*TMath::Poisson(%d, [0])*exp(-0.5*((x - %.1f*[3] - [1])/(%.1f*[4]**2 + [2]**2)**0.5)**2)' % (iElectron, iElectron, iElectron, iElectron) for iElectron in xrange(1, num_photons + 1)]
h_mpe_spec = Hist(*d_mpe_fit['settings'], name='h_mpe_spec', title='MPE Spectrum with Gaussian Fit - %s' % filename)
h_mpe_spec.SetMarkerSize(0)
h_mpe_spec.fill_array(a_integral)
c1 = Canvas()
h_mpe_spec.Draw()
s_bkg = '[5]/(2*3.14*[2]**2.)**0.5*TMath::Poisson(0, [0])*exp(-0.5*((x - [1])/[2])**2)'
s_under_amplified = '[6]/(2*3.14*[8]**2.)**0.5*exp(-0.5*((x - [7])/[8])**2)'
s_fit_mpe = '(%s) + (%s) + (%s)' % (s_bkg, s_under_amplified, ' + '.join(l_mpe_fit_func))
s_fit_mpe = '(%s)*([1] < [3] ? 1. : 0.)*([1] < [7] ? 1. : 0.)*([7] < [3] ? 1. : 0.)' % (s_fit_mpe)
fit_mpe = root.TF1('fit_mpe', s_fit_mpe, *d_mpe_fit['settings'][1:])
fit_mpe.SetLineColor(46)
fit_mpe.SetLineStyle(2)
fit_mpe.SetLineWidth(3)
h_mpe_spec.GetXaxis().SetTitle('Integrated Charge [e-]')
h_mpe_spec.GetYaxis().SetTitle('Counts')
h_mpe_spec.GetYaxis().SetTitleOffset(1.4)
h_mpe_spec.SetStats(0)
c1.SetLogy()
fit_mpe.SetParLimits(0, 0.9, 2.5)
fit_mpe.SetParameter(0, 1.3)
fit_mpe.SetParLimits(1, d_mpe_fit['bkg_mean_low'], d_mpe_fit['bkg_mean_high'])
fit_mpe.SetParameter(1, (d_mpe_fit['bkg_mean_low']+d_mpe_fit['bkg_mean_high'])/2.)
fit_mpe.SetParLimits(2, d_mpe_fit['bkg_width_low'], d_mpe_fit['bkg_width_high'])
fit_mpe.SetParameter(2, (d_mpe_fit['bkg_width_low']+d_mpe_fit['bkg_width_high'])/2.)
fit_mpe.SetParLimits(3, d_mpe_fit['spe_mean_low'], d_mpe_fit['spe_mean_high'])
fit_mpe.SetParameter(3, d_mpe_fit['spe_mean_guess'])
fit_mpe.SetParLimits(4, d_mpe_fit['spe_width_low'], d_mpe_fit['spe_width_high'])
fit_mpe.SetParameter(4, d_mpe_fit['spe_width_guess'])
fit_mpe.SetParLimits(5, 10, max_num_events*1e6)
fit_mpe.SetParameter(5, (10+max_num_events*1e6)/2.)
fit_mpe.SetParLimits(6, 10, max_num_events*1e6)
fit_mpe.SetParameter(6, (10+max_num_events*1e6)/2.)
fit_mpe.SetParLimits(7, d_mpe_fit['ua_mean_low'], d_mpe_fit['ua_mean_high'])
fit_mpe.SetParameter(7, (d_mpe_fit['ua_mean_low']+d_mpe_fit['ua_mean_high'])/2.)
fit_mpe.SetParLimits(8, d_mpe_fit['ua_width_low'], d_mpe_fit['ua_width_high'])
fit_mpe.SetParameter(8, (d_mpe_fit['ua_width_low']+d_mpe_fit['ua_width_high'])/2.)
"""
for i, guess in enumerate(mpe_par_guesses):
fit_mpe.SetParameter(i, guess)
for i in xrange(len(par_names)):
fit_mpe.SetParName(i, par_names[i])
for photon in xrange(num_photons):
fit_mpe.SetParLimits(5 + photon, 0, max_num_events)
"""
fitResult = h_mpe_spec.Fit('fit_mpe', 'MILES')
# draw individual peaks
s_gaussian = '[0]*exp(-0.5/%.1f*((x - %.1f*[1])/[2])**2)'
l_functions = []
l_individual_integrals = [0. for i in xrange(num_photons+2)]
for i in xrange(num_photons + 2):
l_functions.append(root.TF1('peak_%d' % i, '[0]*exp(-0.5*((x - [1])/[2])**2)', *d_mpe_fit['settings'][1:]))
# set parameters
if i == 0:
ampl = fit_mpe.GetParameter(5)*root.TMath.Poisson(0, fit_mpe.GetParameter(0))
mean = fit_mpe.GetParameter(1)
width = fit_mpe.GetParameter(2)
if width > 0:
ampl /= (2*3.14*width**2.)**0.5
l_functions[i].SetParameters(ampl, mean, width)
# under amplified peak
elif i == (num_photons + 1):
ampl = fit_mpe.GetParameter(6)
mean = fit_mpe.GetParameter(7)
width = fit_mpe.GetParameter(8)
if width > 0:
ampl /= (2*3.14*width**2.)**0.5
l_functions[i].SetParameters(ampl, mean, width)
else:
ampl = fit_mpe.GetParameter(5)*root.TMath.Poisson(i, fit_mpe.GetParameter(0))
if use_ideal:
mean = fit_mpe.GetParameter(3) * i
width = fit_mpe.GetParameter(4)*i**0.5
else:
mean = fit_mpe.GetParameter(3)*i + fit_mpe.GetParameter(1)
width = (fit_mpe.GetParameter(4)**2*i + fit_mpe.GetParameter(2)**2)**0.5
if width > 0:
ampl /= (2*3.14*width**2.)**0.5
l_functions[i].SetParameters(ampl, mean, width)
l_individual_integrals[i] = ampl*width*(2*3.1415)**0.5
l_functions[i].SetLineColor(l_colors[i])
l_functions[i].Draw('same')
c1.Update()
fitStatus = fitResult.CovMatrixStatus()
if fitStatus != 3:
neriX_analysis.failure_message('Fit failed, please adjust guesses and try again.')
fit_successful = False
else:
neriX_analysis.success_message('Fit successful, please copy output to appropriate files.')
fit_successful = True
#if not os.path.exists(sPathToSaveOutput):
# os.makedirs(sPathToSaveOutput)
fitter = root.TVirtualFitter.Fitter(fit_mpe)
#fitter = root.TVirtualFitter.GetFitter()
amin = np.asarray([0], dtype=np.float64)
dum1 = np.asarray([0], dtype=np.float64)
dum2 = np.asarray([0], dtype=np.float64)
dum3 = np.asarray([0], dtype=np.int32)
dum4 = np.asarray([0], dtype=np.int32)
fitter.GetStats(amin, dum1, dum2, dum3, dum4)
print '\n\namin for %d photons: %f' % (num_photons, amin)
print 'fAmin for %d photons: %f\n\n' % (num_photons, root.gMinuit.fAmin)
print fit_mpe.GetChisquare()
# draw tpavetext
tpt_mpe = root.TPaveText(.55,.75,.85,.85,'blNDC')
tpt_mpe.AddText('#mu_{SPE} = %.2e #pm %.2e' % (fit_mpe.GetParameter(3), fit_mpe.GetParError(3)))
tpt_mpe.AddText('#sigma_{SPE} = %.2e #pm %.2e' % (fit_mpe.GetParameter(4), fit_mpe.GetParError(4)))
tpt_mpe.Draw('same')
tpt_mpe.SetTextColor(root.kBlack)
tpt_mpe.SetFillStyle(0)
tpt_mpe.SetBorderSize(0)
c1.Update()
neriX_analysis.save_plot(l_plots, c1, 'mpe_poisson_gaussian_fit_%s' % (file_identifier))
return (0,0,0)
for ievt in xrange(10000):
#some test histograms:
#1. let 2 histograms screwed
#h1.Fill(rand.Gaus(0.5, 0.8))
#h2.Fill(rand.Gaus(0, 1))
#2. long tail and short tail
h1.Fill(rand.Gaus(0, 0.8))
h2.Fill(rand.Gaus(0, 1))
#hs = ROOT.THStack("hs", "2 example distributions")
#hs.Add(h1)
#hs.Add(h2)
#hs.Draw("nostack")
h1.Draw()
h2.Draw('same')
#draw legend
leg = ROOT.TLegend(0.7, 0.7, 0.89, 0.89)
leg.SetFillColor(0)
leg.AddEntry(h1, h1.GetTitle(), "pl")
leg.AddEntry(h2, h2.GetTitle(), "pl")
leg.Draw()
can.Modified()
can.Update()
gr = qqplot(h1, h2)
gr.SetLineColor(ROOT.kRed + 2)
gr.SetMarkerColor(ROOT.kRed + 2)
def make_plot():
canvas = Canvas(700, 500)
hist = Hist(10, -3, 3)
hist.FillRandom('gaus')
hist.Draw()
return canvas
