def draw_hists(hists,
field,
category,
textsize=22,
logy=False,
unit_area=False):
xtitle = get_xtitle(field)
c = Canvas()
c.SetGridx()
c.SetGridy()
c.SetLogy(logy)
if not isinstance(hists, (list, tuple)):
hists = [hists]
if unit_area:
for h in hists:
if h.integral() != 0:
h /= h.integral()
hists[0].xaxis.title = xtitle
hists[0].yaxis.title = 'Arbitrary Unit'
y_max = get_ymax(hists)
hists[0].yaxis.SetRangeUser(0., 1.05 * y_max)
hists[0].Draw('HIST')
colors = [
'black',
'red',
'orange',
'blue',
'green',
'purple',
'yellow',
'pink',
]
if len(hists) > len(colors):
colors = len(hists) * colors
for hist, col in zip(hists, colors):
hist.color = col
hist.color = col
hist.fillstyle = 'hollow'
hist.markersize = 0
hist.linewidth = 2
hist.linestyle = 'solid'
hist.drawstyle = 'hist E0'
hist.legendstyle = 'l'
hist.Draw('SAMEHIST')
label = ROOT.TLatex(c.GetLeftMargin() + 0.04, 0.9, category.label)
label.SetNDC()
label.SetTextFont(43)
label.SetTextSize(textsize)
label.Draw()
leg = Legend(hists, pad=c, textsize=20)
# textsize=20, leftmargin=0.6, topmargin=0.6)
leg.Draw('same')
return c
def plot(params, rates):
canvas = Canvas(width=500, height=500)
draw_axes(params)
draw_rates(params, rates)
HGCAL_label(text='HGCAL Simulation', pad=canvas)
canvas.SetLogy()
canvas.RedrawAxis()
canvas.Print('%s.png' % params.name)
canvas.Print('%s.pdf' % params.name)
canvas.Print('%s.C' % params.name)
markercolor='blue')
uct_iso_efficiency = Efficiency(uct_iso_pass_vs_pu,
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)
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
ratio_up = up / nominal
ratio_dn = dn / nominal
ratio_canvas = Canvas()
ratio_canvas.SetLogy()
ratio_up.SetMinimum(0.001)
ratio_up.Draw('hist')
ratio_dn.Draw('same hist')
ratio_up.xaxis.SetTitle('BDT Score')
ratio_up.yaxis.SetTitle('Systematic / Nominal')
ratio_canvas.SaveAs('canvas_ratio.png')
# Now smooth each ratio
ratio_up_smooth = ratio_up.Clone()
ratio_up_smooth.Smooth(args.smooth_iterations)
ratio_dn_smooth = ratio_dn.Clone()
ratio_dn_smooth.Smooth(args.smooth_iterations)
ratio_smooth_canvas = Canvas()
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)
# ymax = getMax( [h_data, h_t1After, h_t2After, h_t3After] )
# h_data.GetYaxis().SetRangeUser(0,ymax)
# h_t1After.GetYaxis().SetRangeUser(0,ymax)
# h_t2After.GetYaxis().SetRangeUser(0,ymax)
# h_t3After.GetYaxis().SetRangeUser(0,ymax)
leg.AddEntry(h_tSumAfter, style='L', label='Sum')
leg.Draw()
c.Update()
if drawScancan:
scancan = Canvas()
scancan.Divide(nTemplates)
scancan.SetLogy()
nCan = 1
if useT1:
scancan.cd(nCan)
# scan1.SetMaximum(scan1.GetMaximum()/100)
scan1.SetMarkerStyle(20)
# scan1.SetMarkerSize(1)
scan1.Draw('AP')
nCan = nCan + 1
pass
if useT2:
scancan.cd(nCan)
scan2.SetMaximum(1000)
scan2.SetMarkerStyle(20)
# scan2.SetMarkerSize(20)
scan2.Draw('AP')
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
def stack(x, *args, **kwargs):
## parse arguments
_data = kwargs.pop('data', None)
_bkgs = kwargs.pop('bkgs', None)
_sigs = kwargs.pop('sigs', None)
_treename = kwargs.pop('treename', None)
_datasearchpath = kwargs.pop('datasearchpath', None)
_datadrivensearchpath = kwargs.pop('datadrivensearchpath', None)
_bkgsearchpath = kwargs.pop('bkgsearchpath', None)
_sigsearchpath = kwargs.pop('sigsearchpath', None)
_lumi = kwargs.pop('lumi', None)
global data
global bkgs
global sigs
global treename
global datasearchpath
global datadrivensearchpath
global bkgsearchpath
global sigsearchpath
global lumi
data = _data or data
bkgs = _bkgs or bkgs
sigs = _sigs or sigs
treename = _treename or treename
datasearchpath = _datasearchpath or datasearchpath
datadrivensearchpath = _datadrivensearchpath or datadrivensearchpath
bkgsearchpath = _bkgsearchpath or bkgsearchpath
sigsearchpath = _sigsearchpath or sigsearchpath
if _lumi:
lumi = float(_lumi)
xtitle = kwargs.pop('xtitle', '')
ytitle = kwargs.pop('ytitle', '')
logx = bool(kwargs.pop('logx', False))
logy = bool(kwargs.pop('logy', False))
blind = kwargs.pop('blind', None)
has_blinded_data = False
## save stuff to bookkeep and return
stuff = dict()
stuff['x'] = x
## get data histogram
h_data = None
if data:
sp = datasearchpath # HACK: just data to True!
newx = '%s::%s::%s' % (sp, treename, x)
h_data = ipyhep.tree.project(newx, *args, **kwargs)
if h_data:
stuff['h_data'] = h_data
## blind the data?
if h_data and not blind is None:
if isinstance(blind, tuple):
blind1, blind2 = blind
nbins = h_data.GetNbinsX()
for i_bin in xrange(1, nbins +
2): # skip underflow (but not overflow)
xval1 = h_data.GetXaxis().GetBinLowEdge(i_bin)
xval2 = h_data.GetXaxis().GetBinUpEdge(i_bin)
if xval1 >= blind1 and xval2 <= blind2:
h_data.SetBinContent(i_bin, 0.0)
h_data.SetBinError(i_bin, 0.0)
has_blinded_data = True
else:
nbins = h_data.GetNbinsX()
for i_bin in xrange(1, nbins +
2): # skip underflow (but not overflow)
xval = h_data.GetXaxis().GetBinLowEdge(i_bin)
if xval >= blind:
h_data.SetBinContent(i_bin, 0.0)
h_data.SetBinError(i_bin, 0.0)
has_blinded_data = True
## get background histograms
h_bkgs = list()
n_bkgs = list()
if bkgs:
for bkg in bkgs:
if isinstance(bkg, list):
h_subtotal = None
for dsid in bkg:
assert isinstance(dsid, str)
h_bkg = None
if dsid.isdigit():
## mc backgrounds
sp = bkgsearchpath % int(dsid)
newx = '%s::%s::%s' % (sp, treename, x)
h_bkg = ipyhep.tree.project(newx, *args, **kwargs)
else:
## data-driven backgrounds
assert dsid == 'fakes' or dsid == 'efakes'
sp = datadrivensearchpath % dsid
newx = '%s::%s::%s' % (sp, treename, x)
h_bkg = ipyhep.tree.project(newx, *args, **kwargs)
if h_bkg:
if h_subtotal:
h_subtotal.Add(h_bkg)
else:
h_subtotal = h_bkg.Clone()
if h_subtotal:
h_bkgs.append(h_subtotal)
dsid = bkg[0]
n_bkgs.append(dsid)
else:
dsid = bkg
assert isinstance(dsid, str)
h_bkg = None
if dsid.isdigit():
## mc backgrounds
sp = bkgsearchpath % int(dsid)
newx = '%s::%s::%s' % (sp, treename, x)
h_bkg = ipyhep.tree.project(newx, *args, **kwargs)
else:
## data-driven backgrounds
assert dsid == 'fakes' or dsid == 'efakes'
sp = datadrivensearchpath % dsid
newx = '%s::%s::%s' % (sp, treename, x)
h_bkg = ipyhep.tree.project(newx, *args, **kwargs)
if h_bkg:
h_bkgs.append(h_bkg)
n_bkgs.append(dsid)
if h_bkgs:
stuff['h_bkgs'] = h_bkgs
## get signal histograms
h_sigs = list()
n_sigs = list()
if sigs:
for dsid in sigs:
sp = sigsearchpath % int(dsid)
newx = '%s::%s::%s' % (sp, treename, x)
h_sig = ipyhep.tree.project(newx, *args, **kwargs)
if h_sig:
h_sigs.append(h_sig)
n_sigs.append(dsid)
if h_sigs:
stuff['h_sigs'] = h_sigs
assert h_sigs
## style data
if h_data:
h_data.title = 'Data'
h_data.linecolor = ipyhep.style.black
h_data.linewidth = 2
h_data.markercolor = ipyhep.style.black
h_data.markerstyle = 20
h_data.markersize = 1.2
h_data.fillstyle = ipyhep.style.fill_hollow
h_data.drawstyle = 'PE'
h_data.legendstyle = 'LP'
## scale and style background histograms
if h_bkgs:
assert len(h_bkgs) == len(n_bkgs), '%s\n%s' % (h_bkgs, n_bkgs)
for h, dsid in zip(h_bkgs, n_bkgs):
sf = ipyhep.sampleops.get_sf(dsid)
if dsid.isdigit():
sf *= lumi / __ntuple_lumi
h.Scale(sf)
h.title = ipyhep.sampleops.get_label(dsid)
h.linecolor = ipyhep.style.black
h.linewidth = 1
h.markercolor = ipyhep.sampleops.get_color(dsid)
h.fillcolor = ipyhep.sampleops.get_color(dsid)
h.fillstyle = ipyhep.style.fill_solid
h.legendstyle = 'F'
## calculate stat error on total background
h_bkg_total = None
if h_bkgs:
for h_bkg in h_bkgs:
if h_bkg_total:
h_bkg_total.Add(h_bkg)
else:
h_bkg_total = h_bkg.Clone()
stuff['h_bkg_total'] = h_bkg_total
## style h_bkg_total
if h_bkg_total:
h_bkg_total.title = 'stat. uncert.'
h_bkg_total.linecolor = ipyhep.style.black
h_bkg_total.linewidth = 1
h_bkg_total.markerstyle = 0
h_bkg_total.fillcolor = ipyhep.style.dark_gray
h_bkg_total.fillstyle = ipyhep.style.fill_lines
h_bkg_total.drawstyle = 'E2'
h_bkg_total.legendstyle = 'LF'
## scale and style signal histograms
if h_sigs:
assert len(h_sigs) == len(n_sigs)
for h, dsid in zip(h_sigs, n_sigs):
sf = ipyhep.sampleops.get_sf(dsid)
sf *= lumi / __ntuple_lumi
h.Scale(sf)
h.title = ipyhep.sampleops.get_label(dsid)
h.linecolor = ipyhep.sampleops.get_color(dsid)
h.linewidth = 3
h.fillstyle = ipyhep.style.fill_hollow
h.markerstyle = 0
h.drawstyle = 'HIST'
h.legendstyle = 'L'
## build list of all_hists
all_hists = list()
main_hists = list()
if h_data:
all_hists.append(h_data)
main_hists.append(h_data)
if h_bkgs:
all_hists.extend(h_bkgs)
main_hists.extend(h_bkgs)
if h_bkg_total:
all_hists.append(h_bkg_total)
main_hists.append(h_bkg_total)
if h_sigs:
all_hists.extend(h_sigs)
## get statistics
if all_hists:
stats_list = list()
for h in all_hists:
stats_list.extend(get_stats(h))
html = convert_table_to_html(convert_stats_to_table(stats_list))
stuff['html'] = html
## renormalize for bin widths
bins = kwargs.pop('bins', None)
if bins and isinstance(bins, list):
for h in all_hists:
renormalize_for_bin_widths(h, bins)
## stack background histograms
if h_bkgs:
assert len(h_bkgs) == len(n_bkgs), '%s\n%s' % (h_bkgs, n_bkgs)
h_bkgs.reverse()
n_bkgs.reverse()
hstack = HistStack()
for h in h_bkgs:
hstack.Add(h)
hstack.title = 'stack sum'
hstack.drawstyle = 'HIST'
stuff['stack'] = hstack
h_bkgs.reverse()
n_bkgs.reverse()
# ## convert data to TGraphAsymmErrors
# g_data = None
# if h_data:
# if __use_poissonize:
# g_data = poissonize.GetPoissonizedGraph(h_data)
# else:
# g_data = ROOT.TGraphAsymmErrors()
# i_g = 0
# nbins = h_data.GetNbinsX()
# for i_bin in xrange(1, nbins+1): # skip underflow/overflow
# c = h_data.GetBinContent(i_bin)
# e = h_data.GetBinError(i_bin)
# if c != 0.0:
# g_data.SetPoint(i_g, h_data.GetBinCenter(i_bin), c)
# g_ratio.SetPointError(i_g,
# h_data.GetBinWidth(i_bin)/2.,
# h_data.GetBinWidth(i_bin)/2.,
# e,
# e)
# i_g += 1
## build list of objects to draw
objects = list()
if h_bkgs:
objects.append(stuff['stack'])
objects.append(stuff['h_bkg_total'])
if h_sigs:
objects.extend(h_sigs)
if h_data:
objects.append(h_data)
## set xlimits and ylimits
ypadding = 0.21
logy_crop_value = 7e-3
xmin, xmax, ymin, ymax = 0.0, 1.0, 0.0, 1.0
if objects:
xmin, xmax, ymin, ymax = get_limits(objects,
logx=logx,
logy=logy,
ypadding=ypadding,
logy_crop_value=logy_crop_value)
if logy:
ymin = 7e-3
else:
ymin = 0.0
xlimits = (xmin, xmax)
ylimits = (ymin, ymax)
stuff['xlimits'] = xlimits
stuff['ylimits'] = ylimits
## remove xtitle for do_ratio
_xtitle = xtitle
if h_data and h_bkg_total and kwargs.get('do_ratio'):
_xtitle = ''
## make canvas
canvas = Canvas(800, 600)
stuff['canvas'] = canvas
## draw the objects
if objects:
canvas.cd()
draw(objects,
pad=canvas,
xtitle=_xtitle,
ytitle=ytitle,
xlimits=xlimits,
ylimits=ylimits)
## set log x/y, for some reason doesn't work before draw
if logx or logy:
if logx:
canvas.SetLogx()
if logy:
canvas.SetLogy()
canvas.Update()
## draw blind_line
if has_blinded_data:
if isinstance(blind, tuple):
blind_list = list(blind)
else:
blind_list = [blind]
blind_lines = list()
for bl in blind_list:
line_y1 = ymin
line_y2 = ymax
blind_line = ROOT.TLine(bl, line_y1, bl, line_y2)
blind_line.SetLineColor(ROOT.kGray + 2)
blind_line.SetLineStyle(7)
blind_line.SetLineWidth(2)
blind_line.Draw()
blind_lines.append(blind_line)
stuff['blind_lines'] = blind_lines
canvas.Update()
## legend
lefty = True
if h_bkg_total:
lefty = is_left_sided(h_bkg_total)
elif h_data:
lefty = is_left_sided(h_data)
elif h_sigs:
lefty = is_left_sided(h_sigs[0])
if main_hists:
header = '%.1f fb^{-1}, 13 TeV' % (lumi / 1000.0)
if lefty:
legend = Legend(main_hists,
pad=canvas,
header=header,
textsize=16,
topmargin=0.03,
leftmargin=0.60,
rightmargin=0.02,
entrysep=0.01,
entryheight=0.04)
else:
legend = Legend(main_hists,
pad=canvas,
header=header,
textsize=16,
topmargin=0.03,
leftmargin=0.03,
rightmargin=0.59,
entrysep=0.01,
entryheight=0.04)
legend.Draw()
stuff['legend'] = legend
if h_sigs:
# header = 'ATLAS Internal'
header = ''
if lefty:
legend2 = Legend(h_sigs,
pad=canvas,
header=header,
textsize=16,
topmargin=0.03,
leftmargin=0.37,
rightmargin=0.23,
entrysep=0.01,
entryheight=0.04)
else:
legend2 = Legend(h_sigs,
pad=canvas,
header=header,
textsize=16,
topmargin=0.03,
leftmargin=0.20,
rightmargin=0.40,
entrysep=0.01,
entryheight=0.04)
legend2.Draw()
stuff['legend2'] = legend2
## do_ratio
if h_data and h_bkg_total and kwargs.get('do_ratio'):
## top canvas
top_canvas = stuff.pop('canvas')
stuff['top_canvas'] = top_canvas
## make SM/SM with error band: h_ratio_band
i_sfratio = int(kwargs.get('sfratio', -1))
if i_sfratio < 0: # ratio plot of Data/Model
h_ratio_band = h_bkg_total.Clone()
nbins = h_ratio_band.GetNbinsX()
for i_bin in xrange(nbins + 2):
h_ratio_band.SetBinContent(i_bin, 1.0)
c = h_bkg_total.GetBinContent(i_bin)
e = h_bkg_total.GetBinError(i_bin) / c if c > 0.0 else 0.0
h_ratio_band.SetBinError(i_bin, e)
stuff['h_ratio_band'] = h_ratio_band
else: # ratio plot of Scale Factor for ith background
hi = h_bkgs[i_sfratio]
h_ratio_band = hi.Clone()
nbins = h_ratio_band.GetNbinsX()
for i_bin in xrange(nbins + 2):
h_ratio_band.SetBinContent(i_bin, 1.0)
c = hi.GetBinContent(i_bin)
e = hi.GetBinError(i_bin) / c if c > 0.0 else 0.0
h_ratio_band.SetBinError(i_bin, e)
stuff['h_ratio_band'] = h_ratio_band
## make data/(SM) h_ratio
if i_sfratio < 0:
h_ratio = h_data.Clone()
h_ratio.Divide(h_data, h_bkg_total, 1.0, 1.0)
stuff['h_ratio'] = h_ratio
else:
## SF1 = 1.0 + (data - MCtot) / MC1
sfname = kwargs.get('sfname')
sffile = kwargs.get('sffile')
if not sfname:
sfname = 'h_sf'
hi = h_bkgs[i_sfratio]
h_numer = h_data.Clone()
h_numer.Add(h_bkg_total, -1.0)
## do the division
h_ratio = h_data.Clone(sfname)
h_ratio.Divide(h_numer, hi, 1.0, 1.0)
## add the 1.0
nbins = h_ratio.GetNbinsX()
for i_bin in xrange(nbins + 2):
c = h_ratio.GetBinContent(i_bin)
h_ratio.SetBinContent(i_bin, c + 1.0)
h_ratio_band.SetBinContent(i_bin, c + 1.0)
## ignore bins with no data for SF
for i_bin in xrange(nbins + 2):
c = h_data.GetBinContent(i_bin)
if c <= 0:
h_ratio.SetBinContent(i_bin, 0.0)
h_ratio.SetBinError(i_bin, 0.0)
h_ratio_band.SetBinError(i_bin, 0.0)
stuff['h_ratio'] = h_ratio
if sffile:
f_out = ipyhep.file.write(h_ratio, sffile)
# f_out.Close()
## convert ratio to a TGraphErrors so that Draw('E0')
## shows error bars for points off the pad
g_ratio = ROOT.TGraphErrors()
i_g = 0
for i_bin in xrange(1, nbins + 1): # skip underflow/overflow
ratio_content = h_ratio.GetBinContent(i_bin)
if ratio_content != 0.0:
g_ratio.SetPoint(i_g, h_ratio.GetBinCenter(i_bin),
ratio_content)
g_ratio.SetPointError(i_g,
h_ratio.GetBinWidth(i_bin) / 2.,
h_ratio.GetBinError(i_bin))
i_g += 1
else:
h_ratio.SetBinError(i_bin, 0.0)
stuff['g_ratio'] = g_ratio
## style ratio
h_ratio_band.title = 'bkg uncert.'
if i_sfratio < 0:
h_ratio_band.linecolor = ipyhep.style.yellow
else:
h_ratio_band.linecolor = ipyhep.style.light_gray
h_ratio_band.linewidth = 0
h_ratio_band.markerstyle = 0
if i_sfratio < 0:
h_ratio_band.fillcolor = ipyhep.style.yellow
else:
h_ratio_band.linecolor = ipyhep.style.light_gray
h_ratio_band.fillstyle = ipyhep.style.fill_solid
h_ratio_band.drawstyle = 'E2'
h_ratio_band.legendstyle = 'F'
h_ratio.title = 'ratio'
h_ratio.linecolor = ipyhep.style.black
h_ratio.linewidth = 2
h_ratio.markercolor = ipyhep.style.black
h_ratio.markerstyle = 20
h_ratio.markersize = 1.2
h_ratio.fillstyle = ipyhep.style.fill_hollow
h_ratio.drawstyle = 'PE'
h_ratio.legendstyle = 'LP'
## bottom canvas
bottom_canvas = Canvas(800, 600)
bottom_canvas.cd()
stuff['bottom_canvas'] = bottom_canvas
## set ratio ylimits
ratio_min = kwargs.get('ratio_min', -0.2)
ratio_max = kwargs.get('ratio_max', 2.2)
ratio_ylimits = (ratio_min, ratio_max)
## draw ratio band
if i_sfratio < 0:
_ytitle = 'Data / Model'
else:
hi = h_bkgs[i_sfratio]
_ytitle = 'SF(%s)' % hi.title
draw([h_ratio_band],
pad=bottom_canvas,
xtitle=xtitle,
ytitle=_ytitle,
xlimits=xlimits,
ylimits=ratio_ylimits)
## set log x/y, for some reason doesn't work before draw?
if logx:
bottom_canvas.SetLogx()
bottom_canvas.Update()
### make horiz lines in ratio plot every 0.5:
line_ys = [
y / 10.0
for y in range(10 *
int(round(ratio_min)), 10 * int(round(ratio_max)) +
5, 5)
]
line_x1 = canvas.GetUxmin()
line_x2 = canvas.GetUxmax()
line_xwidth = abs(line_x2 - line_x1)
lines = []
for line_y in line_ys:
line = ROOT.TLine(line_x1 + 0.02 * line_xwidth, line_y,
line_x2 - 0.02 * line_xwidth, line_y)
line.SetLineWidth(1)
line.SetLineStyle(7)
if line_y == 1.0:
line.SetLineColor(ROOT.kGray + 2)
else:
line.SetLineColor(ROOT.kGray + 0)
line.Draw()
lines.append(line)
stuff['lines'] = lines
## draw blind_line
if has_blinded_data:
if isinstance(blind, tuple):
blind_list = list(blind)
else:
blind_list = [blind]
blind_lines = list()
for bl in blind_list:
line_y1 = ymin
line_y2 = ymax
blind_line = ROOT.TLine(bl, line_y1, bl, line_y2)
blind_line.SetLineColor(ROOT.kGray + 2)
blind_line.SetLineStyle(7)
blind_line.SetLineWidth(2)
blind_line.Draw()
blind_lines.append(blind_line)
stuff['blind_lines2'] = blind_lines
canvas.Update()
## draw ratio
g_ratio.Draw('PE0')
# h_ratio.GetYaxis().SetRangeUser(ratio_min, ratio_max)
# h_ratio.Draw('PE,SAME')
## shared canvas
shared_canvas = Canvas(800, 800)
shared_plot = plot_shared_axis(top_canvas,
bottom_canvas,
canvas=shared_canvas,
split=0.35,
axissep=0.01)
stuff['canvas'] = shared_canvas
canvas = shared_canvas
## save figures
save = kwargs.get('save')
if save is None: # NOTE: save can be False to skip saving
save = ['pdf', 'png']
if save:
ipyhep.file.save_figures(canvas, x, save)
global results
results = stuff
return stuff