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 makeDiscr(discr_dict,
outfile,
xtitle="discriminator",
nbins=30,
x_min=0,
x_max=1):
c = ROOT.TCanvas("c", "c", 800, 500)
ROOT.gStyle.SetOptStat(0)
ROOT.gPad.SetMargin(0.15, 0.1, 0.2, 0.1)
#ROOT.gPad.SetLogy(1)
#ROOT.gPad.SetGrid(1,1)
ROOT.gStyle.SetGridColor(17)
l = TLegend(0.17, 0.75, 0.88, 0.88)
l.SetTextSize(0.055)
l.SetBorderSize(0)
l.SetFillStyle(0)
l.SetNColumns(2)
colors = [2, 4, 8, ROOT.kCyan + 2]
counter = 0
for leg, discr in discr_dict.iteritems():
a = Hist(nbins, x_min, x_max)
#fill_hist_with_ndarray(a, discr)
a.fill_array(discr)
a.SetLineColor(colors[counter])
a.SetLineWidth(2)
a.GetXaxis().SetTitle(xtitle)
a.GetXaxis().SetLabelSize(0.05)
a.GetXaxis().SetTitleSize(0.05)
a.GetXaxis().SetTitleOffset(1.45)
a.GetYaxis().SetTitle("a.u.")
a.GetYaxis().SetTickSize(0)
a.GetYaxis().SetLabelSize(0)
a.GetYaxis().SetTitleSize(0.06)
a.GetYaxis().SetTitleOffset(0.9)
a.Scale(1. / a.Integral())
#a.GetYaxis().SetRangeUser(0.00001,100)
a.GetYaxis().SetRangeUser(0, 0.2)
if counter == 0: a.draw("hist")
else: a.draw("same hist")
l.AddEntry(a, leg, "l")
counter += 1
l.Draw("same")
c.SaveAs(outfile)
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_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_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 rate_test(et_values, event_runs, et_cuts):
events = {}
for et, (run_event) in zip(et_values, event_runs):
event = tuple(run_event)
if event in events:
if et > events[event]: events[event] = et
else: events[event] = et
histo = Hist(256, -0.5, 255.5)
for event, et in events.items():
histo.Fill(et)
total_events = histo.integral(overflow=True)
rates = []
for et_cut in et_cuts:
bin = histo.GetXaxis().FindBin(et_cut)
pass_events = histo.integral(bin, overflow=True)
rates.append([
et_cut,
float(pass_events) / float(total_events) * nbunches * c_light /
lhc_length * 1.e-3
])
return np.array(rates, dtype=np.float64)
gr.SetFillColor(17)
gr.SetFillStyle(1001)
c = Canvas(name="c", title="QQ with CL", width=600, height=450)
gr.Draw("ap")
x_min = gr.GetXaxis().GetXmin()
x_max = gr.GetXaxis().GetXmax()
y_min = gr.GetXaxis().GetXmin()
y_max = gr.GetXaxis().GetXmax()
c.Clear()
gr.Draw('a3')
gr.Draw('Xp same')
# a straight line y=x to be a reference
f_dia = ROOT.TF1("f_dia", "x",
h1.GetXaxis().GetXmin(),
h1.GetXaxis().GetXmax())
f_dia.SetLineColor(9)
f_dia.SetLineWidth(2)
f_dia.SetLineStyle(2)
f_dia.Draw("same")
leg = ROOT.TLegend(0.52, 0.15, 0.87, 0.35)
leg.SetFillColor(0)
leg.SetShadowColor(17)
leg.SetBorderSize(3)
leg.AddEntry(gr, "QQ points", "p")
leg.AddEntry(gr, "68% CL band", "f")
leg.AddEntry(f_dia, "Diagonal line", "l")
leg.Draw()
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()
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()
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)
def make_plot(raw_data, binning, title_text, outfn):
h = Hist(*binning,
drawstyle='hist e1',
color=sigCOLORS[0],
linewidth=2,
title=';Percent difference[%];Events')
for sample, value in raw_data.items():
h.Fill(value['variation'], value['count'])
hc = asrootpy(h.GetCumulative())
hc.linecolor = 'gold'
hc.fillcolor = 'lightyellow'
hc.fillstyle = 'solid'
hc.scale(h.max(include_error=True) / hc.max())
xmin_, xmax_, ymin_, ymax_ = get_limits([h, hc])
draw([hc, h], ylimits=(0, ymax_))
x95, x99 = None, None
cumsum = 0
for i in range(1, h.GetNbinsX() + 1):
cumsum += h.GetBinContent(i)
if x95 is None and cumsum / h.Integral() > 0.95:
x95 = h.GetXaxis().GetBinUpEdge(i)
if x99 is None and cumsum / h.Integral() > 0.99:
x99 = h.GetXaxis().GetBinUpEdge(i)
title = TitleAsLatex(title_text)
title.Draw()
# print(title_text, ROOT.gPad.GetUymax(), hc.max())
# draw a second axis on the right.
ROOT.gPad.SetTicks(
1, 0
) # Draw top ticks but not right ticks (https://root.cern.ch/root/roottalk/roottalk99/2908.html)
low, high = 0, ROOT.gPad.GetUymax() / hc.max()
raxis = ROOT.TGaxis(ROOT.gPad.GetUxmax(), ROOT.gPad.GetUymin(),
ROOT.gPad.GetUxmax(), ROOT.gPad.GetUymax(),
low, high, 510, "+L")
raxis.SetLabelSize(0.03)
raxis.SetLabelFont(42)
raxis.SetLabelColor(convert_color('gold', 'root'))
raxis.Draw()
frame = canvas.FindObject('TFrame')
lo, hi = frame.GetY1(), frame.GetY2()
l95 = ROOT.TLine(x95, lo, x95, hi)
l95.SetLineStyle(2)
l95.SetLineColor(convert_color(sigCOLORS[1], 'root'))
l95.SetLineWidth(2)
l95.Draw()
l99 = ROOT.TLine(x99, lo, x99, hi)
l99.SetLineStyle(3)
l99.SetLineColor(convert_color(sigCOLORS[2], 'root'))
l99.Draw()
leg = Legend(3,
margin=0.25,
leftmargin=0.45,
topmargin=0.02,
entrysep=0.01,
entryheight=0.02,
textsize=10)
leg.AddEntry(hc, label='cumulative (norm.)', style='LF')
leg.AddEntry(l95, label='95% @ {}'.format(x95), style='L')
leg.AddEntry(l99, label='99% @ {}'.format(x99), style='L')
leg.Draw()
canvas.SaveAs(outfn)
canvas.clear()
def makeDiscr(train_discr_dict, discr_dict, outfile, xtitle="discriminator"):
c = ROOT.TCanvas("c", "c", 800, 500)
ROOT.gStyle.SetOptStat(0)
ROOT.gPad.SetMargin(0.15, 0.1, 0.2, 0.1)
#ROOT.gPad.SetLogy(1)
#ROOT.gPad.SetGrid(1,1)
ROOT.gStyle.SetGridColor(17)
l = TLegend(0.17, 0.75, 0.88, 0.88)
l.SetTextSize(0.055)
l.SetBorderSize(0)
l.SetFillStyle(0)
l.SetNColumns(2)
colors = [2, 1, 4, ROOT.kCyan + 2]
counter = 0
for leg, discr in train_discr_dict.iteritems():
a = Hist(30, 0, 1)
#fill_hist_with_ndarray(a, discr)
a.fill_array(discr)
a.SetLineColor(colors[counter])
a.SetLineWidth(2)
a.GetXaxis().SetTitle(xtitle)
a.GetXaxis().SetLabelSize(0.05)
a.GetXaxis().SetTitleSize(0.06)
a.GetXaxis().SetTitleOffset(1.45)
a.GetYaxis().SetTitle("a.u.")
a.GetYaxis().SetTickSize(0)
a.GetYaxis().SetLabelSize(0)
a.GetYaxis().SetTitleSize(0.06)
a.GetYaxis().SetTitleOffset(0.9)
a.Scale(1. / a.Integral())
#a.GetYaxis().SetRangeUser(0.00001,100)
a.GetYaxis().SetRangeUser(0, 0.9)
if counter == 0: a.draw("hist")
else: a.draw("same hist")
l.AddEntry(a, leg, "l")
counter += 1
counter = 0
for leg, discr in discr_dict.iteritems():
a = Hist(30, 0, 1)
#fill_hist_with_ndarray(a, discr)
a.fill_array(discr)
a.SetLineColor(colors[counter])
a.SetMarkerColor(colors[counter])
a.SetMarkerStyle(34)
a.SetMarkerSize(1.8)
a.SetLineWidth(2)
a.GetXaxis().SetTitle(xtitle)
a.GetXaxis().SetLabelSize(0.05)
a.GetXaxis().SetTitleSize(0.06)
a.GetXaxis().SetTitleOffset(1.45)
a.GetYaxis().SetTitle("a.u.")
a.GetYaxis().SetTickSize(0)
a.GetYaxis().SetLabelSize(0)
a.GetYaxis().SetTitleSize(0.06)
a.GetYaxis().SetTitleOffset(0.9)
a.Scale(1. / a.Integral())
#a.GetYaxis().SetRangeUser(0.00001,100)
a.GetYaxis().SetRangeUser(0, 0.4)
a.draw("same p X0")
l.AddEntry(a, leg, "p")
counter += 1
# counter = 0
# for leg,discr in train_discr_dict.iteritems():
# d = Hist(30, 0, 1)
# d.fill_array(discr)
# d.SetLineColor(colors[counter])
# d.SetLineWidth(2)
# l.AddEntry(d,leg,"l")
#
# b = Hist(30, 0, 1)
# d.fill_array(discr_dict[leg.split(" ")[0] + " test"])
# b.SetLineColor(colors[counter])
# b.SetMarkerColor(colors[counter])
# b.SetMarkerStyle(34)
# b.SetMarkerSize(1.8)
# b.SetLineWidth(2)
# l.AddEntry(b,leg,"p")
# counter += 1
l.Draw("same")
c.SaveAs(outfile)
def main(parameters):
input_files = parameters.input_files
tree_name = parameters.input_tree
means = Hist(4*len(input_files), 0, 4*len(input_files), name='mean values')
rmss = Hist(4*len(input_files), 0, 4*len(input_files), name='rms values')
cov_means = Hist(2*len(input_files), 0, 2*len(input_files), name='covariance mean values')
cov_rmss = Hist(2*len(input_files), 0, 2*len(input_files), name='covariance rms values')
means.SetYTitle('Variance mean [cm^{2}]')
rmss.SetYTitle('Variance RMS [cm^{2}]')
cov_means.SetYTitle('Covariance mean [cm^{2}]')
cov_rmss.SetYTitle('Covariance RMS [cm^{2}]')
with root_open(parameters.output_file, 'recreate') as output_file:
for i, (name, input_file_name) in enumerate(input_files):
means.GetXaxis().SetBinLabel(i*4+1, name+' x')
means.GetXaxis().SetBinLabel(i*4+2, name+' y')
means.GetXaxis().SetBinLabel(i*4+3, name+' u')
means.GetXaxis().SetBinLabel(i*4+4, name+' v')
rmss.GetXaxis().SetBinLabel(i*4+1, name+' x')
rmss.GetXaxis().SetBinLabel(i*4+2, name+' y')
rmss.GetXaxis().SetBinLabel(i*4+3, name+' u')
rmss.GetXaxis().SetBinLabel(i*4+4, name+' v')
cov_means.GetXaxis().SetBinLabel(i*2+1, name+' x/y')
cov_rmss.GetXaxis().SetBinLabel(i*2+1, name+' x/y')
cov_means.GetXaxis().SetBinLabel(i*2+2, name+' u/v')
cov_rmss.GetXaxis().SetBinLabel(i*2+2, name+' u/v')
with root_open(input_file_name) as input_file:
tree = input_file.Get(tree_name)
moment_2nd_x, moment_2nd_y, moment_2nd_xy, moment_2nd_u, moment_2nd_v, moment_2nd_uv = shape_distribution(tree, parameters.log_weights)
print name
print 'x variance Mu =', moment_2nd_x.GetMean(), '+/-', moment_2nd_x.GetMeanError(), 'RMS =', moment_2nd_x.GetRMS(), '+/-', moment_2nd_x.GetRMSError()
print 'y variance Mu =', moment_2nd_y.GetMean(), '+/-', moment_2nd_y.GetMeanError(), 'RMS =', moment_2nd_y.GetRMS(), '+/-', moment_2nd_y.GetRMSError()
print 'u variance Mu =', moment_2nd_u.GetMean(), '+/-', moment_2nd_u.GetMeanError(), 'RMS =', moment_2nd_u.GetRMS(), '+/-', moment_2nd_u.GetRMSError()
print 'v variance Mu =', moment_2nd_v.GetMean(), '+/-', moment_2nd_v.GetMeanError(), 'RMS =', moment_2nd_v.GetRMS(), '+/-', moment_2nd_v.GetRMSError()
print 'x-y covariance Mu =', moment_2nd_xy.GetMean(), '+/-', moment_2nd_xy.GetMeanError(), 'RMS =', moment_2nd_xy.GetRMS(), '+/-', moment_2nd_xy.GetRMSError()
print 'u-v covariance Mu =', moment_2nd_uv.GetMean(), '+/-', moment_2nd_uv.GetMeanError(), 'RMS =', moment_2nd_uv.GetRMS(), '+/-', moment_2nd_uv.GetRMSError()
# fill mean values of variances
means.SetBinContent(i*4+1, moment_2nd_x.GetMean())
means.SetBinError(i*4+1, moment_2nd_x.GetMeanError())
means.SetBinContent(i*4+2, moment_2nd_y.GetMean())
means.SetBinError(i*4+2, moment_2nd_y.GetMeanError())
means.SetBinContent(i*4+3, moment_2nd_u.GetMean())
means.SetBinError(i*4+3, moment_2nd_u.GetMeanError())
means.SetBinContent(i*4+4, moment_2nd_v.GetMean())
means.SetBinError(i*4+4, moment_2nd_v.GetMeanError())
# fill rms values of variances
rmss.SetBinContent(i*4+1, moment_2nd_x.GetRMS())
rmss.SetBinError(i*4+1, moment_2nd_x.GetRMSError())
rmss.SetBinContent(i*4+2, moment_2nd_y.GetRMS())
rmss.SetBinError(i*4+2, moment_2nd_y.GetRMSError())
rmss.SetBinContent(i*4+3, moment_2nd_u.GetRMS())
rmss.SetBinError(i*4+3, moment_2nd_u.GetRMSError())
rmss.SetBinContent(i*4+4, moment_2nd_v.GetRMS())
rmss.SetBinError(i*4+4, moment_2nd_v.GetRMSError())
# fill mean values of covariances
cov_means.SetBinContent(i*2+1, moment_2nd_xy.GetMean())
cov_means.SetBinError(i*2+1, moment_2nd_xy.GetMeanError())
cov_means.SetBinContent(i*2+2, moment_2nd_uv.GetMean())
cov_means.SetBinError(i*2+2, moment_2nd_uv.GetMeanError())
# fill rms values of covariances
cov_rmss.SetBinContent(i*2+1, moment_2nd_xy.GetRMS())
cov_rmss.SetBinError(i*2+1, moment_2nd_xy.GetRMSError())
cov_rmss.SetBinContent(i*2+2, moment_2nd_uv.GetRMS())
cov_rmss.SetBinError(i*2+2, moment_2nd_uv.GetRMSError())
# write variance distributions
moment_2nd_x.SetName(moment_2nd_x.GetName()+'_'+name)
moment_2nd_y.SetName(moment_2nd_y.GetName()+'_'+name)
moment_2nd_xy.SetName(moment_2nd_xy.GetName()+'_'+name)
moment_2nd_u.SetName(moment_2nd_u.GetName()+'_'+name)
moment_2nd_v.SetName(moment_2nd_v.GetName()+'_'+name)
output_file.cd()
moment_2nd_x.Write()
moment_2nd_y.Write()
moment_2nd_xy.Write()
moment_2nd_u.Write()
moment_2nd_v.Write()
means.Write()
rmss.Write()
cov_means.Write()
cov_rmss.Write()
