def _do_raw_1d(data, prelim=False):
colors = [ROOT.kBlack, ROOT.kRed, ROOT.kBlue]
for npart, layer in itertools.product(range(1,4), [n for n, _ in LCONDS]):
ldata = data[layer]['Output_number'][
np.where(data[layer]['Output_number_true'] == npart)
]
ldata /= np.sum(ldata, axis=1).reshape((ldata.shape[0], 1))
canvas = ROOT.TCanvas(
'c_raw_{}_{}'.format(npart, layer),
'',
0,
0,
800,
600
)
canvas.SetLogy()
leg = ROOT.TLegend(0.7, 0.7, 0.9, 0.85)
leg.SetBorderSize(0)
for i_out in range(3):
hist = ROOT.TH1D(
'h_raw_{}_{}_{}'.format(npart, layer, i_out),
'',
30,
0,
1
)
ROOT.SetOwnership(hist, False)
root_numpy.fill_hist(hist, ldata[:, i_out])
hist.Scale(1.0 / hist.Integral())
hist.SetLineColor(colors[i_out])
hist.SetMaximum(10)
hist.Draw(('' if i_out == 0 else 'same') + 'HIST')
leg.AddEntry(hist, '{}-particle bin'.format(i_out + 1), 'L')
leg.Draw()
figures.draw_atlas_label(preliminary=prelim)
txt = ROOT.TLatex()
txt.SetNDC()
txt.SetTextSize(txt.GetTextSize() * 0.75)
txt.DrawLatex(0.2, 0.82, 'PYTHIA8 dijet, 1.8 < p_{T}^{jet} < 2.5 TeV')
txt.DrawText(
0.2,
0.77,
'{}-particle clusters'.format(npart)
)
txt.DrawText(0.2, 0.72, layer)
canvas.SaveAs('number_raw_{}_{}.pdf'.format(npart, layer))
def _plot_2d(hsdict, variable, nparticle, layer, preliminary):
name = '_'.join([variable, str(nparticle), '2D', layer])
key = name
if key not in hsdict:
return
th2 = hsdict[name]
_set_palette()
ROOT.gStyle.SetNumberContours(255)
canvas = ROOT.TCanvas('canvas_' + name, '', 0, 0, 800, 600)
th2.Rebin2D(2, 2)
th2.GetZaxis().SetTitle("Particles / {x} x {y} {u}".format(
x=th2.GetXaxis().GetBinWidth(1),
y=th2.GetYaxis().GetBinWidth(1),
u='mm^{2}' if 'residual' in variable else ''))
th2.GetZaxis().SetLabelSize(0.04)
ROOT.TGaxis.SetMaxDigits(4)
if 'residual' in variable:
th2.GetXaxis().SetRangeUser(-0.05, 0.05)
th2.GetYaxis().SetRangeUser(-0.5, 0.5)
else:
th2.GetXaxis().SetRangeUser(-5, 5)
th2.GetYaxis().SetRangeUser(-5, 5)
th2.GetXaxis().SetLabelSize(0.04)
th2.GetYaxis().SetLabelSize(0.04)
th2.SetTitle(';Truth hit local x {v} {u};Truth hit local y {v} {u}'.format(
v=variable.replace('corr_', '').rstrip('s'),
u='[mm]' if 'residual' in variable else ''))
th2.Draw('COLZ')
figures.draw_atlas_label(preliminary)
txt = ROOT.TLatex()
txt.SetNDC()
txt.SetTextSize(txt.GetTextSize() * 0.75)
txt.DrawLatex(0.2, 0.82, 'PYTHIA8 dijet, 1.8 < p_{T}^{jet} < 2.5 TeV')
txt.DrawText(
0.2, 0.77,
'{}-particle{} clusters'.format(nparticle,
'' if nparticle == 1 else 's'))
txt.DrawText(
0.2, 0.72,
layer.upper() if layer == 'ibl' else layer[0].upper() + layer[1:])
canvas.SaveAs(name + '.pdf')
def _roc_graph(data, classes, prelim=False):
# pylint: disable=too-many-locals
pos, neg = classes
canvas = ROOT.TCanvas('c_{}vs{}.format(pos, neg)', '', 0, 0, 800, 600)
canvas.SetLogx()
graphs = ROOT.TMultiGraph()
leg = ROOT.TLegend(0.63, 0.7, 0.9, 0.88)
for layer in data:
if pos == 3:
pos_sel = data[layer]['Output_number_true'] >= pos
else:
pos_sel = data[layer]['Output_number_true'] == pos
if neg == 3:
neg_sel = data[layer]['Output_number_true'] >= neg
else:
neg_sel = data[layer]['Output_number_true'] == neg
isel = np.where(
np.logical_or(
pos_sel,
neg_sel,
)
)[0]
fpr, tpr, _ = sklearn.metrics.roc_curve(
data[layer]['Output_number_true'][isel],
data[layer]['Output_number'][isel][:, pos - 1],
pos_label=pos
)
auc = sklearn.metrics.auc(fpr, tpr)
graph = ROOT.TGraph(fpr.size)
_set_graph_style(graph, layer)
root_numpy.fill_graph(graph, np.column_stack((fpr, tpr)))
graphs.Add(graph)
leg.AddEntry(graph, '{}, AUC: {:.2f}'.format(layer, auc), 'L')
graphs.SetTitle(
';Pr(Estimated: {pos}-particle{ps} | True: {neg}-particle{ns});Pr(Estimated: {pos}-particle{ps} | True: {pos}-particle{ps})'.format( # noqa
pos=pos,
neg=neg,
ps=('' if pos == 1 else 's'),
ns=('' if neg == 1 else 's')
)
)
graphs.SetMaximum(1.5)
graphs.Draw('AL')
line = ROOT.TF1("line", "x", 0, 1)
line.SetLineStyle(3)
line.SetLineColor(ROOT.kGray + 2)
line.Draw("same")
leg.AddEntry(line, 'Random, AUC: 0.50', 'L')
leg.SetTextSize(leg.GetTextSize() * 0.65)
leg.SetBorderSize(0)
leg.Draw()
figures.draw_atlas_label(prelim)
txt = ROOT.TLatex()
txt.SetNDC()
txt.SetTextSize(txt.GetTextSize() * 0.75)
txt.DrawLatex(0.2, 0.82, 'PYTHIA8 dijet, 1.8 < p_{T}^{jet} < 2.5 TeV')
canvas.SaveAs('ROC_{}vs{}.pdf'.format(pos, neg))
def _do_raw_2d(data, x=2, y=3, prelim=False):
x_min, x_max = 0, 1
y_min, y_max = 0, 1.4
z_min, z_max = 1e-6, 1
x_n_bins = 50
y_n_bins = int(x_n_bins * (y_max - y_min) / (x_max - x_min))
oldmargin = ROOT.gStyle.GetPadRightMargin()
ROOT.gStyle.SetPadRightMargin(0.15)
_set_palette()
ROOT.gStyle.SetNumberContours(255)
for npart in range(1, 4):
canvas = ROOT.TCanvas('c', '', 0, 0, 800, 600)
canvas.SetLogz()
hist = ROOT.TH2D(
'h_raw_2D_{}'.format(npart),
'',
x_n_bins,
x_min,
x_max,
y_n_bins,
y_min,
y_max
)
hist.GetYaxis().SetRangeUser(0, y_max)
hist.GetZaxis().SetLabelSize(0.04)
hist.GetZaxis().SetTitle(
"Cluster density / {x} x {y}".format(
x=hist.GetXaxis().GetBinWidth(1),
y=hist.GetYaxis().GetBinWidth(1),
)
)
hist.SetTitle(
';{x}-particle score;{y}-particle score'.format(
x=x if x !=3 else '#geq {}'.format(x),
y=y if y !=3 else '#geq {}'.format(y),
)
)
ROOT.TGaxis.SetMaxDigits(4)
for layer in [n for n, _ in LCONDS]:
layer_data = data[layer]['Output_number'][
np.where(data[layer]['Output_number_true'] == npart)
]
root_numpy.fill_hist(hist, layer_data[:,[x-1,y-1]])
hist.Scale(1.0 / hist.Integral())
hist.SetMinimum(z_min)
hist.SetMaximum(z_max)
hist.Draw('COLZ')
# Plot the classification region
if x == 2 and y == 3:
gr1 = ROOT.TGraph(4, array.array('d', [0, 0, 0.6, 0.6]), array.array('d', [0, 0.2, 0.2, 0]))
gr1.SetFillColorAlpha(ROOT.kBlack, 0.2)
gr1.SetLineWidth(0)
gr1.Draw('F')
# Add legend
leg = ROOT.TLegend(0.5, 0.7, 0.8, 0.8)
leg.SetTextSize(0.02)
leg.SetBorderSize(0)
leg.AddEntry(gr1, "1-particle classification region", "F")
leg.Draw()
# Add text
figures.draw_atlas_label(preliminary=prelim)
txt = ROOT.TLatex()
txt.SetNDC()
txt.SetTextSize(txt.GetTextSize() * 0.75)
txt.DrawLatex(0.2, 0.82, 'PYTHIA8 dijet, 1.8 < p_{T}^{jet} < 2.5 TeV')
txt.DrawText(
0.2,
0.77,
'{}-particle clusters'.format(npart)
)
canvas.SaveAs('number_raw_{}_{}_2D_{}.pdf'.format(x, y, npart))
ROOT.gStyle.SetPadRightMargin(oldmargin)
def _tpr_fnr(data, pos, cond, preliminary=False):
ROOT.gStyle.SetErrorX(0.5)
colors = {1: ROOT.kBlack, 2: ROOT.kRed, 3: ROOT.kBlue}
markers = {1: 8, 2: 21, 3: 23}
oldmargin = ROOT.gStyle.GetPadRightMargin()
ROOT.gStyle.SetPadRightMargin(0.15)
if pos == 1:
fnrs = [2, 3]
elif pos == 2:
fnrs = [1, 3]
else:
fnrs = [1, 2]
# define bins in eta
bins = _get_bins(cond)
bin_data_1 = np.zeros(len(bins) - 1)
bin_data_2or3 = np.zeros(len(bins) - 1)
e_bin_data_1 = np.zeros(len(bins) - 1)
e_bin_data_2or3 = np.zeros(len(bins) - 1)
for i in range(1, len(bins)):
data_1 = np.zeros(3)
data_2or3 = np.zeros(3)
e_data_1 = np.zeros(3)
e_data_2or3 = np.zeros(3)
stats = np.zeros(3)
for j, layer in enumerate(data.keys()):
# get the indices corresponding to eta bins
i_bins = np.digitize(data[layer][cond], bins)
bin_data = data[layer][np.where(i_bins == i)]
data_1[j], e_data_1[j] = _calc_rate(bin_data, true=pos, est=[1])
data_2or3[j], e_data_2or3[j] = _calc_rate(bin_data, true=pos, est=[2,3])
stats[j] = bin_data.shape[0]
if np.all(stats == 0):
weights = 0
else:
weights = stats / float(np.sum(stats))
bin_data_1[i-1] = np.sum(weights * data_1)
bin_data_2or3[i-1] = np.sum(weights * data_2or3)
e_bin_data_1[i-1] = np.sqrt(np.sum(weights*weights*e_data_1*e_data_1))
e_bin_data_2or3[i-1] = np.sqrt(np.sum(weights*weights*e_data_2or3*e_data_2or3))
hist_1 = ROOT.TH1F(
'h',
'',
len(bins) - 1,
array.array('f', bins)
)
hist_2or3 = ROOT.TH1F(
'ha',
'',
len(bins) - 1,
array.array('f', bins)
)
root_numpy.fill_hist(hist_1, bins[:-1], weights=bin_data_1)
root_numpy.fill_hist(hist_2or3, bins[:-1], weights=bin_data_2or3)
for i in range(1, bins.shape[0]):
hist_1.SetBinError(i, e_bin_data_1[i-1])
hist_2or3.SetBinError(i, e_bin_data_2or3[i-1])
cnv = ROOT.TCanvas('c', '', 0, 0, 800, 600)
# cnv.SetLogy()
hist_1.SetTitle(';{};Pr(Estimated | true: {} particle)'.format(
_get_xlabel(cond),
pos,
))
xmax_1 = 1.6
hist_1.SetMaximum(xmax_1)
hist_1.SetMinimum(0.0)
hist_1.SetLineColor(colors[1])
hist_1.SetMarkerColor(colors[1])
hist_1.SetMarkerStyle(markers[1])
hist_1.Draw('p e')
cnv.Update()
hist_2or3.SetLineColor(colors[2])
hist_2or3.SetMarkerColor(colors[2])
hist_2or3.SetMarkerStyle(markers[2])
hist_2or3.Draw('p e same')
if cond == 'cluster_size':
hist_1.GetXaxis().SetNdivisions(4, False)
for k,v in {1: 1, 2: 5, 3: 10, 4: 15, 5: 20}.iteritems():
hist_1.GetXaxis().ChangeLabel(k, -1, -1, -1, -1, -1, str(v))
if cond in ['cluster_size_X', 'cluster_size_Y']:
hist_1.GetXaxis().SetNdivisions(7, False)
hist_1.GetXaxis().CenterLabels()
figures.draw_atlas_label(preliminary)
txt = ROOT.TLatex()
txt.SetNDC()
txt.SetTextSize(txt.GetTextSize() * 0.75)
txt.DrawLatex(0.2, 0.82, 'PYTHIA8 dijet, 1.8 < p_{T}^{jet} < 2.5 TeV')
txt.DrawText(
0.2,
0.77,
'{}-particle{} clusters'.format(pos, '' if pos == 1 else 's')
)
leg = ROOT.TLegend(0.6, 0.7, 0.84, 0.82)
leg.SetBorderSize(0)
leg.AddEntry(hist_1, 'Estimated = 1', 'PL')
leg.AddEntry(hist_2or3, 'Estimated = 2 or 3', 'PL')
leg.Draw()
cnv.SaveAs('tpr_fnr_{}_{}.pdf'.format(pos, cond))
ROOT.gStyle.SetPadRightMargin(oldmargin)
def _plot_2d_cond(hsdict, variable, cond, direction, prelim):
if direction is not None:
name = '_'.join([variable, direction, '2D', cond])
else:
name = '_'.join([variable, '2D', cond])
canvas = ROOT.TCanvas("canvas_" + name, '', 0, 0, 800, 600)
legend = ROOT.TLegend(0.7, 0.7, 0.9, 0.85)
legend.SetBorderSize(0)
colors = {1: ROOT.kBlack, 2: ROOT.kRed, 3: ROOT.kBlue}
for nparticle in [1, 2, 3]:
# First, build an inclusive histogram with all the layers
hist_incl = None
for lyr in LAYERS:
if direction is not None:
hname = '_'.join(
[variable,
str(nparticle), direction, '2D', cond, lyr])
else:
hname = '_'.join([variable, str(nparticle), '2D', cond, lyr])
if hname in hsdict:
if hist_incl is None:
hist_incl = hsdict[hname]
else:
hist_incl.Add(hsdict[hname])
if hist_incl is None:
continue
# Now, iterate through bins of the conditional variables in
# the right range
bins = []
binvals = []
binerrs = []
vmin, vmax = _get_range(cond)
for i in range(hist_incl.GetXaxis().FindBin(vmin),
hist_incl.GetXaxis().FindBin(vmax)):
proj = hist_incl.ProjectionY("proj%d%d" % (i, nparticle), i, i)
if 'residuals' in variable:
sigma, dsigma = _fwhm(proj, err=True)
else:
(_, _, sigma), (_, _, dsigma) = _fit(proj, err=True)
binvals.append(sigma)
binerrs.append(dsigma)
bins.append(hist_incl.GetXaxis().GetBinLowEdge(i))
bins.append(hist_incl.GetXaxis().GetBinLowEdge(i + 1))
bins, binvals, binerrs = _rebin(bins, binvals, binerrs, cond)
hist = ROOT.TH1D(
'h_%d_%s_%s_%s' % (nparticle, direction, cond, variable), '',
len(bins) - 1, array.array('d', bins))
ROOT.SetOwnership(hist, False)
root_numpy.fill_hist(hist, bins[:-1], binvals)
for i in range(1, bins.shape[0]):
hist.SetBinError(i, binerrs[i - 1])
hist.SetMarkerColor(colors[nparticle])
hist.SetLineColor(colors[nparticle])
hist.SetMarkerStyle(MARKERS[nparticle - 1])
_set_range(hist, variable, direction, cond)
if nparticle == 1:
_set_range(hist, variable, direction, cond)
hist.SetTitle(';{};{}'.format(
_varlabel(cond)[0],
'Residual fwhm [mm]'
if 'residuals' in variable else 'Pull #sigma',
))
if cond == 'cluster_size':
hist.GetXaxis().SetNdivisions(4, False)
for k, v in {1: 1, 2: 5, 3: 10, 4: 15, 5: 20}.iteritems():
hist.GetXaxis().ChangeLabel(k, -1, -1, -1, -1, -1, str(v))
if cond in ['cluster_size_X', 'cluster_size_Y']:
hist.GetXaxis().SetNdivisions(7, False)
hist.GetXaxis().CenterLabels()
hist.Draw(('' if nparticle == 1 else 'same') + ' P E')
legend.AddEntry(
hist,
"{}-particle{} clusters".format(nparticle,
's' if nparticle > 1 else ''))
legend.Draw()
figures.draw_atlas_label(prelim)
txt = ROOT.TLatex()
txt.SetNDC()
txt.SetTextSize(txt.GetTextSize() * 0.75)
txt.DrawLatex(0.2, 0.82, 'PYTHIA8 dijet, 1.8 < p_{T}^{jet} < 2.5 TeV')
if direction is not None:
txt.DrawText(0.2, 0.77, 'Local {} direction'.format(direction.lower()))
# TODO add text for residuals1{X,Y} plots
canvas.SaveAs(name + '.pdf')
def _plot_1d(hsdict, variable, nparticle, direction, preliminary):
# pylint: disable=too-many-locals
name = '_'.join([variable, str(nparticle), direction])
canvas = ROOT.TCanvas('canvas_' + name, '', 0, 0, 800, 600)
legend = ROOT.TLegend(0.61, 0.6, 0.91, 0.85)
legend.SetBorderSize(0)
stack = ROOT.THStack('stk_' + name, '')
gausses = []
for layer, color, marker in zip(LAYERS, COLORS, MARKERS):
LOG.debug((name, layer))
key = name + '_' + layer
if key not in hsdict:
continue
hist = hsdict[key]
if layer == 'endcap' and nparticle == 1:
hist.Rebin(4)
hist.Scale(0.5 / hist.Integral())
else:
hist.Rebin(2)
hist.Scale(1.0 / hist.Integral())
width = hist.GetBinWidth(1)
hist.SetLineColor(color)
hist.SetMarkerColor(color)
hist.SetMarkerStyle(marker)
if 'pull' in variable:
hist.SetLineStyle(2)
const, mean, sigma = _fit(hist)
gausses.append(
ROOT.TF1('f' + layer, '[0]*TMath::Gaus(x, [1], [2])', -5, 5))
gausses[-1].SetLineColor(color)
gausses[-1].SetLineStyle(2)
gausses[-1].SetParameter(0, const)
gausses[-1].SetParameter(1, mean)
gausses[-1].SetParameter(2, sigma)
else:
mean = hist.GetMean()
sigma = _fwhm(hist)
stack.Add(hist)
legend.AddEntry(
hist, '#splitline{%s clusters}'
'{#mu = %.2f %s, %s = %.2f %s}' % (
_layer_name(layer),
mean,
'mm' if 'residuals' in variable else '',
"fwhm" if 'residuals' in variable else "#sigma",
sigma,
'mm' if 'residuals' in variable else '',
), 'LP')
stack.SetTitle(';Truth hit {v} {bu};Particle density / {w} {wu}'.format(
v=variable.replace('corr_', '').rstrip('s'),
bu='[mm]' if 'residuals' in variable else '',
w=width,
wu='mm' if 'residuals' in variable else ''))
if 'pull' in variable:
stack.Draw('p nostack')
for g in gausses:
g.Draw('same')
else:
stack.Draw('hist nostack')
stack.Draw('p same nostack')
scaley = 1.3
if nparticle > 1:
scaley = 1.7
if 'pull' in variable:
rangex = 5.0
else:
if direction == 'X':
if nparticle == 1:
rangex = 0.04
else:
rangex = 0.05
else:
rangex = 0.4
print 'SETTING RANGE AT ' + str(rangex)
stack.GetXaxis().SetRangeUser(-rangex, rangex)
stack.SetMaximum(0.25)
figures.draw_atlas_label(preliminary)
legend.Draw()
txt = ROOT.TLatex()
txt.SetNDC()
txt.SetTextSize(txt.GetTextSize() * 0.75)
txt.DrawLatex(0.2, 0.79, 'PYTHIA8 dijet, 1.8 < p_{T}^{jet} < 2.5 TeV')
txt.DrawText(
0.2, 0.74,
'{}-particle{} clusters'.format(nparticle,
'' if nparticle == 1 else 's'))
txt.DrawText(0.2, 0.69, 'local {} direction'.format(direction.lower()))
canvas.SaveAs(name + '.pdf')