Esempio n. 1
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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)
Esempio n. 2
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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)
Esempio n. 3
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def makeDiscr(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, 4, 8, ROOT.kCyan + 2, ROOT.kBlack, ROOT.kGreen + 3]
    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.SetLineWidth(2)
        a.GetXaxis().SetTitle(xtitle)
        a.GetXaxis().SetLabelSize(0.05)
        a.GetXaxis().SetTitleSize(0.05)
        a.GetXaxis().SetTitleOffset(1.45)
        a.GetYaxis().SetTitle("counts")
        a.GetYaxis().SetTitleSize(0.06)
        a.GetYaxis().SetTitleOffset(0.9)
        a.GetYaxis().SetRangeUser(1, 14000)
        if counter == 0: a.draw("hist")
        else: a.draw("same hist")
        l.AddEntry(a, leg, "l")
        counter += 1

    l.Draw("same")
    c.SetLogy()
    c.SaveAs(outfile)
Esempio n. 4
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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()
Esempio n. 5
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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()
Esempio n. 6
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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()
Esempio n. 7
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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)
Esempio n. 8
0
#     h_t2.SetBinError( bin, sqrt(h_t2.GetBinContent(bin)))
#     h_t3.SetBinError( bin, sqrt(h_t3.GetBinContent(bin)))
#     h_t4.SetBinError( bin, sqrt(h_t4.GetBinContent(bin)))
#     pass

# Make pretty
h_t1.SetLineColor(4)
h_t2.SetLineColor(8)
h_t3.SetLineColor(6)
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 = {}
Esempio n. 9
0
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)