def get_uncertainty_graph(hnom, curve_uncert):
"""
Convert an histogram and a RooCurve
into a TGraphAsymmError
Parameters
----------
hnom: TH1F, TH1D, ...
The histogram of nominal values
curve_uncert: RooCurve
The uncertainty band around the nominal value
TODO: Improve the handling of the underflow and overflow bins
"""
graph = Graph(hnom.GetNbinsX())
for ibin in xrange(1, hnom.GetNbinsX() + 1):
uncerts = []
for ip in xrange(3, curve_uncert.GetN() - 3):
x, y = ROOT.Double(0.), ROOT.Double(0.)
curve_uncert.GetPoint(ip, x, y)
if hnom.GetBinLowEdge(ibin) <= x < hnom.GetBinLowEdge(ibin + 1):
uncerts.append(y)
log.debug('{0}, bin {1}: {2}'.format(hnom.name, ibin, uncerts))
low, high = min(uncerts), max(uncerts)
bin_center = 0.5 * (hnom.GetBinLowEdge(ibin + 1) +
hnom.GetBinLowEdge(ibin))
e_x_low = bin_center - hnom.GetBinLowEdge(ibin)
e_x_high = hnom.GetBinLowEdge(ibin + 1) - bin_center
bin_content = hnom.GetBinContent(ibin)
e_y_low = hnom.GetBinContent(ibin) - low
e_y_high = high - hnom.GetBinContent(ibin)
graph.SetPoint(ibin - 1, bin_center, bin_content)
graph.SetPointError(ibin - 1, e_x_low, e_x_high, e_y_low, e_y_high)
return graph
def spread_x( histograms, bin_edges ):
"""
Usually when plotting multiple histograms with same x-values and
similar y-values their markers will overlap. This function spreads
the data points across a bin. It creates a set of graphs with the
same y-values but different x.
@param histograms: list of histograms with same binning
@param bin_edges: the bin edges of the histograms
"""
# construct bins from the bin edges
bins = [( bin_lower, bin_upper ) for bin_lower, bin_upper in izip( bin_edges[:-1], bin_edges[1:] )]
# now get the bin widths
bin_widths = [abs( bin_i[1] - bin_i[0] ) for bin_i in bins]
# number of histograms
number_of_hists = len( histograms )
# and divide the bins into equidistant bits leaving some space to the bin edges
x_locations = []
add_locations = x_locations.append
for bin_lower, width in izip( bin_edges, bin_widths ):
x_step = width / ( 1.0 * number_of_hists + 1 ) # +1 due to spacing to bin edge
add_locations( [bin_lower + n * x_step for n in range( 1, number_of_hists + 1 )] )
# transpose
x_locations = map( list, zip( *x_locations ) )
graphs = []
for histogram, x_coordinates in zip( histograms, x_locations ):
g = Graph( histogram )
for i, ( x, y ) in enumerate( zip( x_coordinates, histogram.y() ) ):
g.SetPoint( i, x, y )
graphs.append( g )
return graphs
def tau_from_L_curve(unfoldingObject):
'''
Get best tau via l curve method
Not tested
'''
lCurve = TGraph()
logTauX = TSpline3()
logTauY = TSpline3()
iBest = unfoldingObject.ScanLcurve(500, 0., 0., lCurve, logTauX, logTauY)
# Additional info, plots
t = Double(0)
x = Double(0)
y = Double(0)
logTauX.GetKnot(iBest, t, x)
logTauY.GetKnot(iBest, t, y)
bestLcurve = Graph(1)
bestLcurve.SetPoint(1, x, y)
lCurve.SetMarkerColor(600)
lCurve.SetMarkerSize(1)
lCurve.SetMarkerStyle(5)
# lCurve.Draw("AP");
bestLcurve.markercolor = 'red'
# bestLcurve.Draw("*");
return unfoldingObject.GetTau()
def old_working_points(ana, category, wp_level):
log.info('create the workers')
names = ['loose', 'medium', 'tight']
cuts = [
wp_level + '_is_loose == 1', wp_level + '_is_medium == 1',
wp_level + '_is_tight == 1'
]
workers = [FuncWorker(get_sig_bkg, ana, category, cut) for cut in cuts]
run_pool(workers, n_jobs=-1)
yields = [w.output for w in workers]
log.info('--> Calculate the total yields')
sig_tot = ana.tau.events(category)[1].value
bkg_tot = ana.jet.events(category, weighted=True)[1].value
gr = Graph(len(cuts))
wps = []
for i, (val, yields, name) in enumerate(zip(cuts, yields, names)):
eff_sig = yields[0] / sig_tot
eff_bkg = yields[1] / bkg_tot
rej_bkg = 1. / eff_bkg if eff_bkg != 0 else 0
wps.append(working_point(val, eff_sig, eff_bkg, name=name))
gr.SetPoint(i, eff_sig, rej_bkg)
return gr, wps
def grrScale(gr1, scale):
x1 = Double()
x_ = Double()
x1e = Double()
y1 = Double()
y1e = Double()
y = []
ex = []
ey = []
NC = gr1.GetN()
x = [0 for i in range(NC)]
for ii in range(NC):
x1e = gr1.GetErrorX(ii)
y1e = gr1.GetErrorY(ii)
gr1.GetPoint(ii, x_, y1)
x1 = x_ * 1.0
x[ii] = x1
y.append(y1 * scale)
ex.append(x1e)
ey.append(y1e * scale)
gr = Graph(NC)
for x0, y0, x0e, y0e, i in zip(x, y, ex, ey, range(NC)):
gr.SetPoint(i, x0, y0)
gr.SetPointError(i, x0e, x0e, y0e, y0e)
return gr
def roc(ana, category, discr_var):
"""
Calculates the ROC curve
Returns the sorted list of wp and a TGraph
"""
h_template = Hist(1000, 0, 1)
h_sig = ana.tau.get_hist_array({discr_var: h_template}, category=category)
h_sig = h_sig[discr_var]
h_bkg = ana.jet.get_hist_array({discr_var: h_template}, category=category)
h_bkg = h_bkg[discr_var]
roc_gr = Graph(h_sig.GetNbinsX())
roc_list = []
for i in range(1, h_sig.GetNbinsX()):
eff_sig_i = (h_sig.Integral() -
h_sig.Integral(0, i)) / h_sig.Integral()
eff_bkg_i = (h_bkg.Integral() -
h_bkg.Integral(0, i)) / h_bkg.Integral()
rej_bkg_i = 1. / eff_bkg_i if eff_bkg_i != 0 else 0.
roc_list.append(
working_point(h_sig.GetBinLowEdge(i), eff_sig_i, eff_bkg_i))
roc_gr.SetPoint(i, eff_sig_i, rej_bkg_i)
return roc_gr, roc_list
def efficiency_graph(pass_function,
function_inputs,
xs,
bins=None,
error=0.005):
pass_results = pass_function(function_inputs)
if bins is None: # Automatic binning
# Compute the number of bins such that the error on the efficiency is equal to 'error' in each bin
# The calculation is based on binomial errors and assumes that the efficiency is flat (that the distributions of all and selected events are the same)
k = float(np.count_nonzero(pass_results))
n = float(len(pass_results))
percentiles = [0., 100.]
if k > 0:
nbins = (error * n)**2 / k / (1 - k / n)
# Compute the bin bounaries with the same number of events in all bins
percentiles = np.arange(0., 100., 100. / nbins)
percentiles[-1] = 100.
bins = np.unique(np.percentile(xs, percentiles))
# Fill histograms of selected and all events and compute efficiency
histo_pass = Hist(bins)
histo_total = Hist(bins)
fill_hist(histo_pass, xs, pass_results)
fill_hist(histo_total, xs)
efficiency = Graph()
efficiency.Divide(histo_pass, histo_total)
return efficiency
def luminosity_vs_time(timing_list, luminosity_list, style, run_name):
'''
:param timing_list: Python list containing the timing data
:param luminosity_list: Python list containing the luminosity data
:param style: ROOT style in string, such as 'ATLAS'
:return:
'''
# Set ROOT graph style
set_style(str(style))
# create graph
graph = Graph(len(timing_list))
for i, (xx, yy) in enumerate(zip(timing_list, luminosity_list)):
graph.SetPoint(i, xx, yy)
# set visual attributes
graph.markercolor = 'blue'
graph.xaxis.SetTitle("Time")
graph.yaxis.SetTitle("Luminosity")
graph.xaxis.SetRangeUser(min(timing_list), max(timing_list))
graph.yaxis.SetRangeUser(min(luminosity_list), max(luminosity_list))
# plot with ROOT
canvas = Canvas()
graph.Draw("AP")
label = ROOT.TText(0.8, 0.9, str(run_name))
label.SetTextFont(43)
label.SetTextSize(25)
label.SetNDC()
label.Draw()
canvas.Modified()
canvas.Update()
wait(True)
def tau_from_L_curve( unfoldingObject ):
'''
Get best tau via l curve method
Not tested
'''
lCurve = TGraph()
logTauX = TSpline3()
logTauY = TSpline3()
iBest = unfoldingObject.ScanLcurve(500, 0., 0., lCurve, logTauX, logTauY);
# Additional info, plots
t = Double(0)
x = Double(0)
y = Double(0)
logTauX.GetKnot(iBest,t,x);
logTauY.GetKnot(iBest,t,y);
bestLcurve = Graph(1);
bestLcurve.SetPoint(1,x,y);
lCurve.SetMarkerColor(600)
lCurve.SetMarkerSize(1)
lCurve.SetMarkerStyle(5)
# lCurve.Draw("AP");
bestLcurve.markercolor = 'red'
# bestLcurve.Draw("*");
return unfoldingObject.GetTau()
def remap_x_values(hist, corr_hist):
"""
Map the x values of hist to the y values of map_hist.
In order to do so, it is necessary that the x values of hist are also present as x-values in map_hist.
Parameters
----------
hist : Hist1D
corr_hist : Hist2D
Correlations between the quantity on hist's x-axis (also corr_hist's xaxis) and the new
quantity to plot agains (on corr_hist's y-axis.
Returns
-------
Graph
Graph of the remapped hist. Errors are ??? TODO
"""
hist = asrootpy(hist)
corr_hist = asrootpy(corr_hist)
profx = asrootpy(corr_hist.ProfileX(gen_random_name()))
rt_graph = Graph()
for i, (nch_ref_bin,
counter_bin) in enumerate(zip(profx.bins(), hist.bins())):
rt_graph.SetPoint(i, nch_ref_bin.value, counter_bin.value)
xerr, yerr = nch_ref_bin.error / 2.0, counter_bin.error / 2.0
rt_graph.SetPointError(i, xerr, xerr, yerr, yerr)
return rt_graph
def plot_raw_detector_vs_detector(detector_one_data, detector_two_data, style, name):
# Set ROOT graph style
set_style(str(style))
detector_one_list = []
detector_two_list = []
for block in range(len(detector_one_data)):
for bcid in range(len(detector_one_data[block])):
detector_one_list.append(detector_one_data[block][bcid])
detector_two_list.append(detector_two_data[block][bcid])
# create graph
graph = Graph(len(detector_one_list))
for i, (xx, yy) in enumerate(zip(detector_one_list, detector_two_list)):
graph.SetPoint(i, float(xx), float(yy))
# set visual attributes
graph.markercolor = 'blue'
graph.xaxis.SetTitle("BCM V [Raw Rate]")
graph.yaxis.SetTitle("LUCID BI [Raw Rate]")
graph.xaxis.SetRangeUser(0, 1)
graph.yaxis.SetRangeUser(0, 1)
# plot with ROOT
canvas = Canvas()
graph.Draw("AP")
label = ROOT.TText(0.6, 0.9, str(name))
label.SetTextFont(43)
label.SetTextSize(25)
label.SetNDC()
label.Draw()
canvas.Modified()
canvas.Update()
wait(True)
def plot_percent_luminosity_ratio(detector_one_data, detector_two_data, style, run_name):
'''
:param detector_one_data: Data from one detector type, such as ATLAS' LUCID, in a list of lists, every entry is one
luminsoity block, with the luminosity block being a list of BCID data, assumed to be same
length as detector_two_data
:param detector_two_data: Data from another detector type, such as ATLAS' LUCID, in a list of lists, every entry is
one luminosity block, with the luminosity block being a list of BCID data, assumed to be same
length as detector_one_data
:param style: The ROOT style for the graph, generally 'ATLAS'
:return: ROOT plots of the ratio of luminosities over the luminosity, as percetnage difference from first data point
'''
# Set ROOT graph style
set_style(str(style))
print("Number of Luminosity Blocks included: " + str(len(detector_one_data)))
# Get ratio of the detectors
luminosity_ratio = []
lumi_blocks = []
for block in range(len(detector_one_data)):
for bcid in range(len(detector_one_data[block])):
detector_one_point = detector_one_data[block][bcid]
detector_two_point = detector_two_data[block][bcid]
# Check if the blocks are zero
if detector_one_point != 0.0 and detector_two_point != 0.0:
ratio = -math.log(1 - detector_one_point) / -math.log(1 - detector_two_point)
luminosity_ratio.append(ratio)
lumi_blocks.append(block)
# Get percentage difference based off the first block and BCID
first_point = luminosity_ratio[0]
for index in range(len(luminosity_ratio)):
luminosity_ratio[index] = (luminosity_ratio[index] / first_point) - 1
# create graph
graph = Graph(len(lumi_blocks), title=run_name)
for i, (xx, yy) in enumerate(zip(lumi_blocks, luminosity_ratio)):
graph.SetPoint(i, float(xx), float(yy))
# set visual attributes
graph.markercolor = 'blue'
graph.xaxis.SetTitle("Luminosity Block")
graph.yaxis.SetTitle("Luminosity [Percent Ratio]")
graph.xaxis.SetRangeUser(min(lumi_blocks), max(lumi_blocks))
graph.yaxis.SetRangeUser(min(luminosity_ratio), max(luminosity_ratio))
# plot with ROOT
canvas = Canvas()
graph.Draw("AP")
label = ROOT.TText(0.8, 0.9, str(run_name))
label.SetTextFont(43)
label.SetTextSize(25)
label.SetNDC()
label.Draw()
canvas.Modified()
canvas.Update()
wait(True)
def rejection(eff):
htot = asrootpy(eff.GetTotalHistogram()).Clone()
hpass = asrootpy(eff.GetPassedHistogram())
if hpass.Integral != 0:
rej = htot / hpass
else:
rej = htot
rej = Graph(rej)
name = '_'.join(eff.name.split('_')[1:])
rej.name = 'rej_{0}'.format(name)
rej.title = eff.title
return rej
def rejection(eff):
htot = asrootpy(eff.GetTotalHistogram()).Clone()
hpass = asrootpy(eff.GetPassedHistogram())
if hpass.Integral !=0:
rej = htot/hpass
else:
rej = htot
rej = Graph(rej)
name = '_'.join(eff.name.split('_')[1:])
rej.name = 'rej_{0}'.format(name)
rej.title = eff.title
return rej
def test_xerr():
g = Graph(10)
list(g.xerr())
g = Graph(10, type='errors')
list(g.xerr())
g = Graph(10, type='asymm')
list(g.xerr())
def tau_from_scan( unfoldingObject, regularisation_settings ):
variable = regularisation_settings.variable
# Plots that get outputted by the scan
lCurve = TGraph()
scanResult = TSpline3()
d = 'signal'
a = ''
# Parameters of scan
# Number of points to scan, and min/max tau
nScan = 1000
minTau = 1.E-6
maxTau = 1.E-0
if variable == 'abs_lepton_eta':
minTau = 1.E-8
maxTau = 1.E-3
elif variable == 'lepton_pt':
minTau = 1.E-6
maxTau = 1.E-2
elif variable == 'NJets':
minTau = 1.E-6
maxTau = 1.E-2
# Scan is performed here
iBest = unfoldingObject.ScanTau(nScan, minTau, maxTau, scanResult, TUnfoldDensity.kEScanTauRhoSquareAvg);
# Plot the scan result
# Correlation as function of log tau
canvas = TCanvas()
scanResult.SetMarkerColor(600)
scanResult.SetMarkerSize(1)
scanResult.SetMarkerStyle(5)
scanResult.Draw('LP')
# Add point corresponding to optimum tau
t = Double(0)
x = Double(0)
scanResult.GetKnot(iBest,t,x);
bestTau = Graph(1)
bestTau.SetPoint(1,t,x)
bestTau.markercolor = 'red'
bestTau.SetMarkerSize(1.25)
bestTau.Draw('*')
# Write to file
output_dir = regularisation_settings.output_folder
make_folder_if_not_exists(output_dir)
canvas.SaveAs(output_dir + '/{0}.png'.format(variable) )
return unfoldingObject.GetTau()
def draw_curve_8(_func, name, title, ylow, yhigh, num_errs=1):
""" Draw 8TeV trigger efficiency curves """
graphs = []
for (trigger, color) in triggers:
tool = ROOT.TrigTauEfficiency()
tool.loadInputFile(os.path.join(base, 'triggerSF_{0}.root'.format(trigger)))
func = getattr(tool, _func)
eff = np.array(map(lambda x: func(x, eta, 0, period, prong, wpflag, eveto), pt))
errs_low = []
errs_high = []
for ierr in xrange(num_errs):
eff_low = np.array(map(lambda x: func(x, eta, -1, period, prong, wpflag, eveto), pt))
eff_high = np.array(map(lambda x: func(x, eta, 1, period, prong, wpflag, eveto), pt))
errs_low.append(eff_low)
errs_high.append(eff_high)
# quadrature sum of error
eff_low = np.sqrt(np.sum([np.power(err, 2) for err in errs_low], axis=0))
eff_high = np.sqrt(np.sum([np.power(err, 2) for err in errs_high], axis=0))
graph = Graph(len(pt), name=trigger)
for i, (p, e, e_low, e_high) in enumerate(zip(pt, eff, eff_low, eff_high)):
graph.SetPoint(i, p / 1000, e)
graph.SetPointError(i, 0.4, 0.4, e_low, e_high)
graph.linecolor = color
graph.linewidth = 2
graph.fillstyle = '/'
graph.fillcolor = color
graphs.append(graph)
c = Canvas()
leg = Legend(len(graphs),
pad=c, topmargin=0.6, leftmargin=0.3,
textsize=25, margin=0.2)
for i, g in enumerate(graphs):
if i == 0:
g.Draw('3AL')
g.xaxis.title = '#font[52]{p}_{T} [GeV]'
g.xaxis.SetLimits(20, 100)
g.yaxis.SetLimits(ylow, yhigh)
g.yaxis.SetRangeUser(ylow, yhigh)
g.yaxis.title = title
else:
g.Draw('3L SAME')
leg.AddEntry(g, g.name, 'L')
leg.Draw()
lines = []
for thresh in (25, 35):
line = Line(thresh, ylow, thresh, yhigh)
line.linestyle = 'dashed'
line.linewidth = 2
line.Draw()
lines.append(line)
c.SaveAs('trigger_{0}.png'.format(name))
c.SaveAs('trigger_{0}.eps'.format(name))
def test_init_from_file_1d():
with tempfile.NamedTemporaryFile() as f:
for i in xrange(100):
f.write('{0:.3f},{1:.3f}\n'.format(random(), random()))
f.flush()
g = Graph.from_file(f.name, sep=',')
assert_equal(len(g), 100)
def test_errorbar():
from rootpy.plotting import root2matplotlib as rplt
h = Hist(100, -5, 5)
h.FillRandom('gaus')
g = Graph(h)
rplt.errorbar(g)
rplt.errorbar(h)
def rate(et, thresholds, total_events):
pass_thresholds = pass_threshold(et, thresholds)
# fill pass and total numbers of events
bins = (pass_thresholds[:-1, 0] + pass_thresholds[1:, 0]) / 2.
bins = np.append([-bins[0] + 2. * pass_thresholds[0, 0]], bins)
bins = np.append(bins, [2. * pass_thresholds[-1, 0] - bins[-1]])
histo_pass = Hist(bins)
histo_total = Hist(bins)
for b, n in zip(histo_pass[1:-1], pass_thresholds[:, 1]):
b.value = n
b.error = np.sqrt(n)
for b in histo_total[1:-1]:
b.value = total_events
b.error = np.sqrt(total_events)
rates = Graph()
rates.Divide(histo_pass, histo_total)
return rates
def get_rebinned_graph(graph_origin, binning=None):
if binning is None:
return graph_origin
graph_rebin = Graph(len(binning) - 1)
if len(graph_origin) != len(graph_rebin):
log.warning('uniform: {0} bins != rebinned: {1} bins'.format(
len(graph_origin), len(graph_rebin)))
raise RuntimeError('wrong binning')
for ipoint, (y,
yerr) in enumerate(zip(graph_origin.y(),
graph_origin.yerr())):
x_rebin_err = 0.5 * (binning[ipoint + 1] - binning[ipoint])
x_rebin = binning[ipoint] + x_rebin_err
graph_rebin.SetPoint(ipoint, x_rebin, y)
graph_rebin.SetPointError(ipoint, x_rebin_err, x_rebin_err, yerr[0],
yerr[1])
return graph_rebin
def test_init_from_file_1d():
with tempfile.NamedTemporaryFile() as f:
for i in xrange(100):
f.write('{0:.3f},{1:.3f}\n'.format(
random(), random()))
f.flush()
g = Graph.from_file(f.name, sep=',')
assert_equal(len(g), 100)
def double_profile(tprofile_x, tprofile_y):
"""creates a graph with points whose x and y values and errors are taken from the bins of two profiles with identical binning"""
## Note: underflow and overflow bins are discarded
if len(tprofile_x) != len(tprofile_y):
raise ValueError(
"Cannot build double profile: x and y profiles "
"have different number or bins ({} and {})".format(
len(tprofile_x) - 2,
len(tprofile_y) - 2))
_dp_graph = Graph(len(tprofile_x) - 2,
type='errors') # symmetric errors
_i_point = 0
for _i_bin, (_bin_proxy_x,
_bin_proxy_y) in enumerate(zip(tprofile_x, tprofile_y)):
# disregard overflow/underflow bins
if _i_bin == 0 or _i_bin == len(tprofile_x) - 1:
continue
if _bin_proxy_y.value:
_dp_graph.SetPoint(_i_point, _bin_proxy_x.value,
_bin_proxy_y.value)
_dp_graph.SetPointError(_i_point, _bin_proxy_x.error,
_bin_proxy_y.error)
_i_point += 1
# remove "unfilled" points
while (_dp_graph.GetN() > _i_point):
_dp_graph.RemovePoint(_dp_graph.GetN() - 1)
return _dp_graph
def UncertGraph(hnom, curve_uncert):
"""
Convert an histogram and a RooCurve
into a TGraphAsymmError
Parameters
----------
hnom: TH1F, TH1D,...
The histogram of nominal values
curve_uncert: RooCurve
The uncertainty band around the nominal value
curve_uncert: RooCurve
TODO: Improve the handling of the underflow and overflow bins
"""
graph = Graph(hnom.GetNbinsX())
# ---------------------------------------------
for ibin in xrange(1, hnom.GetNbinsX() + 1):
uncerts = []
for ip in xrange(3, curve_uncert.GetN() - 3):
x, y = ROOT.Double(0.), ROOT.Double(0.)
curve_uncert.GetPoint(ip, x, y)
if int(x) == int(hnom.GetBinLowEdge(ibin)):
uncerts.append(y)
uncerts.sort()
log.info('{0}: {1}'.format(hnom.name, uncerts))
if len(uncerts) != 2:
for val in uncerts:
if val in uncerts:
uncerts.remove(val)
if len(uncerts) != 2:
raise RuntimeError(
'Need exactly two error values and got {0}'.format(uncerts))
bin_center = 0.5 * (hnom.GetBinLowEdge(ibin + 1) +
hnom.GetBinLowEdge(ibin))
e_x_low = bin_center - hnom.GetBinLowEdge(ibin)
e_x_high = hnom.GetBinLowEdge(ibin + 1) - bin_center
bin_content = hnom.GetBinContent(ibin)
e_y_low = hnom.GetBinContent(ibin) - uncerts[0]
e_y_high = uncerts[1] - hnom.GetBinContent(ibin)
graph.SetPoint(ibin - 1, bin_center, bin_content)
graph.SetPointError(ibin - 1, e_x_low, e_x_high, e_y_low, e_y_high)
# ---------------------------------------------
return graph
def test_operators():
h = Hist(10, -2, 2).FillRandom('gaus')
g = Graph(h)
g *= 2
g /= 2
g += 2
g -= 2
for point in g:
assert_equal(point.y.value, h[point.idx_ + 1].value)
def draw_curve_7(_func, name, title, ylow, yhigh):
""" Draw 7TeV trigger efficiency curves """
graphs = []
for (trigger, color) in triggers:
tool = ROOT.TauTriggerCorrections(os.path.join(base, 'triggerSF_%s.root' % trigger))
func = getattr(tool, _func)
eff = map(lambda x: func(x, 0), pt)
eff_low = map(lambda x: func(x, -1), pt)
eff_high = map(lambda x: func(x, 1), pt)
graph = Graph(len(pt), name=trigger)
for i, (p, e, e_low, e_high) in enumerate(zip(pt, eff, eff_low, eff_high)):
graph.SetPoint(i, p / 1000, e)
graph.SetPointError(i, 0.4, 0.4, e - e_low, e_high - e)
graph.linecolor = color
graph.linewidth = 2
graph.fillstyle = '/'
graph.fillcolor = color
graphs.append(graph)
c = Canvas()
leg = Legend(len(graphs),
pad=c, topmargin=0.4, leftmargin=0.3, textsize=25, margin=0.2)
for i, g in enumerate(graphs):
if i == 0:
g.Draw('3AC')
g.xaxis.title = '#font[52]{p}_{T} [GeV]'
g.xaxis.SetLimits(20, 100)
g.yaxis.SetLimits(ylow, yhigh)
g.yaxis.SetRangeUser(ylow, yhigh)
g.yaxis.title = title
else:
g.Draw('3C SAME')
leg.AddEntry(g, g.name, 'L')
leg.Draw()
lines = []
for thresh in (25, 35):
line = Line(thresh, ylow, thresh, yhigh)
line.linestyle = 'dashed'
line.linewidth = 2
line.Draw()
lines.append(line)
c.SaveAs('trigger_{0}.png'.format(name))
c.SaveAs('trigger_{0}.eps'.format(name))
def scaleGraph(gr1, sc):
x1 = ROOT.Double()
x_ = ROOT.Double()
y1 = ROOT.Double()
test = []
y = []
NC = gr1.GetN()
x = [0 for i in range(NC)]
for ii in range(NC):
gr1.GetPoint(ii, x_, y1)
x1 = x_ * 1.0
x[ii] = x1
y.append(y1 * sc)
gr = Graph(NC)
for x0, y0, i in zip(x, y, range(NC)):
gr.SetPoint(i, x0, y0)
return gr
def get_ratios(results_7TeV, results_8TeV):
ratios = {}
for key in results_7TeV.keys():
ratio = None
if 'Graph' in str(type(results_7TeV[key])):
ratio = Graph.divide(results_7TeV[key], results_8TeV[key], False)
else:
ratio = results_7TeV[key].Clone( 'ratio_' + key )
ratio.Divide(results_8TeV[key])
ratios[key] = ratio
return ratios
def get_ratios(results_7TeV, results_8TeV):
ratios = {}
for key in results_7TeV.keys():
ratio = None
if 'Graph' in str(type(results_7TeV[key])):
ratio = Graph.divide(results_7TeV[key], results_8TeV[key], False)
else:
ratio = results_7TeV[key].Clone('ratio_' + key)
ratio.Divide(results_8TeV[key])
ratios[key] = ratio
return ratios
def efficiency_bdt(pass_function, function_inputs, xs):
pass_results = pass_function(function_inputs)
xs_train = xs.reshape(-1, 1)
clf = GradientBoostingClassifier()
clf.fit(xs_train, pass_results)
graph = Graph(100)
xs_graph = np.linspace(np.amin(xs), np.amax(xs), num=100)
probas = clf.predict_proba(xs_graph.reshape(-1, 1))[:, [1]]
print probas
fill_graph(graph, np.column_stack((xs_graph, probas)))
print np.column_stack((xs_graph, probas))
return graph
def makeSystError(gr1, gr2, **kwargs):
x_ = Double()
x1e = Double()
x2 = Double()
x2e = Double()
y1 = Double()
y1e = Double()
y2 = Double()
y2e = Double()
NC = gr1.GetN()
print("Number of points: {}".format(NC))
y = []
ex = []
ey = []
x = [0 for i in range(NC)]
for ii in range(NC):
x1e = gr1.GetErrorX(ii)
y1e = gr1.GetErrorY(ii)
gr1.GetPoint(ii, x_, y1)
x1 = x_ * 1.0
gr2.GetPoint(ii, x2, y2)
x2e = gr2.GetErrorX(ii)
y2e = gr2.GetErrorY(ii)
x[ii] = x1
if (kwargs.get('abs', False)):
y.append(math.fabs(y1 - y2))
elif (kwargs.get('rel', False)):
y.append((y1 - y2) / (y1 + y2))
else:
y.append(y1 - y2)
ex.append(x1e)
ey.append(math.sqrt(y1e**2 + y2e**2))
gr = Graph(NC)
for x0, y0, x0e, y0e, i in zip(x, y, ex, ey, range(NC)):
gr.SetPoint(i, x0, y0)
print("Set point {} {},{}".format(i, x0, y0))
gr.SetPointError(i, x0e, x0e, y0e, y0e)
return gr
def grrDivide(gr1, gr2):
x1 = Double()
x_ = Double()
x1e = Double()
x2 = Double()
x2e = Double()
y1 = Double()
y1e = Double()
y2 = Double()
y2e = Double()
test = []
y = []
ex = []
ey = []
NC = gr1.GetN()
x = [0 for i in range(NC)]
test = [0 for i in range(NC)]
for ii in range(NC):
x1e = gr1.GetErrorX(ii)
y1e = gr1.GetErrorY(ii)
gr1.GetPoint(ii, x_, y1)
x1 = x_ * 1.0
gr2.GetPoint(ii, x2, y2)
x2e = gr2.GetErrorX(ii)
y2e = gr2.GetErrorY(ii)
x[ii] = x1
y.append(y1 / y2 if y2 != 0 else 0)
ex.append(x2e)
ey.append(
math.sqrt(pow(y1e / y2, 2) + pow(y1 * y2e /
(y2 * y2), 2)) if y2 != 0 else 0)
gr = Graph(NC)
for x0, y0, x0e, y0e, i in zip(x, y, ex, ey, range(NC)):
gr.SetPoint(i, x0, y0)
gr.SetPointError(i, x0e, x0e, y0e, y0e)
return gr
def find_best_working_point(effs, signal_efficiencies,
background_efficiencies):
# Compute efficiency gradients
effs_diff = np.ediff1d(effs)
signal_efficiencies_diff = np.ediff1d(signal_efficiencies)
background_efficiencies_diff = np.ediff1d(background_efficiencies)
signal_efficiencies_diff = np.divide(signal_efficiencies_diff, effs_diff)
background_efficiencies_diff = np.divide(background_efficiencies_diff,
effs_diff)
# Interpolate and find points where background efficiency gradient > signal efficiency gradient (with some tolerance)
interp_x = np.linspace(np.amin(effs[1:]), np.amax(effs[1:]), 1000)
interp_signal = np.interp(interp_x, effs[1:], signal_efficiencies_diff)
interp_background = np.interp(interp_x, effs[1:],
background_efficiencies_diff)
optimal_points = np.argwhere(
np.greater(interp_background - 0.05, interp_signal)).ravel(
) # Use a tolerance of 0.02 in case of fluctuations
if len(optimal_points) == 0:
print 'WARNING: no working point found where the efficiency gradient is larger for background than for signal'
# Find optimal point with smallest efficiency
## Compute spacing between points, and select those with an efficiency separation > 2%
optimal_discontinuities = np.argwhere(
np.ediff1d(interp_x[optimal_points]) > 0.02).ravel()
## Select the point with the largest efficiency
optimal_index = np.amax(optimal_discontinuities) + 1 if len(
optimal_discontinuities) > 0 else 0
optimal_point = interp_x[optimal_points[optimal_index]]
# Create graphs
signal_efficiencies_diff_graph = Graph(len(effs) - 1)
background_efficiencies_diff_graph = Graph(len(effs) - 1)
optimal_points_graph = Graph(len(optimal_points))
fill_graph(signal_efficiencies_diff_graph,
np.column_stack((effs[1:], signal_efficiencies_diff)))
fill_graph(background_efficiencies_diff_graph,
np.column_stack((effs[1:], background_efficiencies_diff)))
fill_graph(
optimal_points_graph,
np.column_stack(
(interp_x[optimal_points], interp_signal[optimal_points])))
signal_efficiencies_diff_graph.SetName('efficiencies_signal')
background_efficiencies_diff_graph.SetName('efficiencies_background')
optimal_points_graph.SetName('signal_background_optimal_points')
return signal_efficiencies_diff_graph, background_efficiencies_diff_graph, optimal_points_graph, optimal_point
def draw_curve_7(_func, name, title, ylow, yhigh):
""" Draw 7TeV trigger efficiency curves """
graphs = []
for (trigger, color) in triggers:
tool = ROOT.TauTriggerCorrections(
os.path.join(base, 'triggerSF_%s.root' % trigger))
func = getattr(tool, _func)
eff = map(lambda x: func(x, 0), pt)
eff_low = map(lambda x: func(x, -1), pt)
eff_high = map(lambda x: func(x, 1), pt)
graph = Graph(len(pt), name=trigger)
for i, (p, e, e_low,
e_high) in enumerate(zip(pt, eff, eff_low, eff_high)):
graph.SetPoint(i, p / 1000, e)
graph.SetPointError(i, 0.4, 0.4, e - e_low, e_high - e)
graph.linecolor = color
graph.linewidth = 2
graph.fillstyle = '/'
graph.fillcolor = color
graphs.append(graph)
c = Canvas()
leg = Legend(len(graphs),
pad=c,
topmargin=0.4,
leftmargin=0.3,
textsize=25,
margin=0.2)
for i, g in enumerate(graphs):
if i == 0:
g.Draw('3AC')
g.xaxis.title = '#font[52]{p}_{T} [GeV]'
g.xaxis.SetLimits(20, 100)
g.yaxis.SetLimits(ylow, yhigh)
g.yaxis.SetRangeUser(ylow, yhigh)
g.yaxis.title = title
else:
g.Draw('3C SAME')
leg.AddEntry(g, g.name, 'L')
leg.Draw()
lines = []
for thresh in (25, 35):
line = Line(thresh, ylow, thresh, yhigh)
line.linestyle = 'dashed'
line.linewidth = 2
line.Draw()
lines.append(line)
c.SaveAs('trigger_{0}.png'.format(name))
c.SaveAs('trigger_{0}.eps'.format(name))
def test_xerr():
g = Graph(10)
list(g.xerr())
g = Graph(10, type='errors')
list(g.xerr())
g = Graph(10, type='asymm')
list(g.xerr())
def luminosity_block_log_time(luminosity_list, style):
'''
:param timing_list: Python list containing the timing data
:param luminosity_list: Python list containing the luminosity data
:param style: ROOT style in string, such as 'ATLAS'
:return: Graph of the luminosity over a single block
'''
# Set ROOT graph style
set_style(str(style))
luminosity_ratio = []
for bcid in range(len(luminosity_list)):
detector_one_point = luminosity_list[bcid]
# Check if the blocks are zero
if detector_one_point != 0.0:
ratio = -math.log(1 - detector_one_point)
luminosity_ratio.append(ratio)
# create graph
graph = Graph(len(luminosity_ratio))
for i, (xx, yy) in enumerate(zip(range(len(luminosity_ratio)), luminosity_ratio)):
graph.SetPoint(i, xx, yy)
# set visual attributes
graph.markercolor = 'blue'
graph.xaxis.SetTitle("Time")
graph.yaxis.SetTitle("-Ln(1 - Rate) [Single Detector]")
graph.xaxis.SetRangeUser(0, 3564)
graph.yaxis.SetRangeUser(min(luminosity_ratio), max(luminosity_ratio))
# plot with ROOT
canvas = Canvas()
graph.Draw("AP")
wait(True)
def get_mean_rms(category, var):
gr_mean = Graph(len(SIGNALS_14TEV))
gr_rms = Graph(len(SIGNALS_14TEV))
for ip, signal in enumerate(SIGNALS_14TEV):
with root_open('efficiencies/eff_presel_{0}_v{1}.root'.format(
signal, VERSION)) as fsig:
h_s = fsig[category].Get('h_' + category + '_' + var['name'])
gr_mean.SetPoint(ip, DATASETS[signal]['mu'], h_s.GetMean())
gr_mean.SetPointError(ip, 0, 0, h_s.GetMeanError(),
h_s.GetMeanError())
gr_rms.SetPoint(ip, DATASETS[signal]['mu'], h_s.GetRMS())
gr_rms.SetPointError(ip, 0, 0, h_s.GetRMSError(),
h_s.GetRMSError())
gr_mean.xaxis.title = 'Average Interactions Per Bunch Crossing'
gr_mean.yaxis.title = 'Mean of ' + get_label(var)
gr_rms.xaxis.title = 'Average Interactions Per Bunch Crossing'
gr_rms.yaxis.title = 'RMS of ' + get_label(var)
return gr_mean, gr_rms
def addConstantError(gr1, error, **kwargs):
x_ = Double()
x1e = Double()
y1_ = Double()
y1e = Double()
NC = gr1.GetN()
y2 = [0 for i in range(NC)]
ex = []
ey = []
x = [0 for i in range(NC)]
gr = Graph(NC)
for ii in range(NC):
x1e = gr1.GetErrorX(ii)
y1e = gr1.GetErrorY(ii)
gr1.GetPoint(ii, x_, y1_)
print("{} {} {}".format(i, x_, y1_))
x1 = x_ * 1.0
x[ii] = x1
y1 = y1_ * 1.0
y2[ii] = y1
print("Append y with {}".format(y1))
print(y2)
ex.append(x1e)
if (kwargs.get('rel'), False):
ey.append(math.sqrt(y1e**2 + (error * y1)**2))
print("Old error: {}, New Error: {}".format(
y1e, math.sqrt(y1e**2 + (error * y1)**2)))
else:
ey.append(math.sqrt(y1e**2 + error**2))
# gr.SetPoint(i,x1,y1)
# print("Set point {} {},{}".format(i,x1,y1))
#
# if(kwargs.get('rel'),False):
# gr.SetPointError(i,x1e,x1e,math.sqrt(y1e**2+(error*y1)**2),math.sqrt(y1e**2+(error*y1)**2))
# print("Set point Error {} {},{}".format(i,x1e,math.sqrt(y1e**2+(error*y1)**2)))
# else:
# gr.SetPointError(i,x1e,x1e,math.sqrt(y1e**2+error**2),math.sqrt(y1e**2+error**2))
gr = Graph(NC)
print(x)
print(y2)
print(ex)
print(ey)
for x0, y0, x0e, y0e, i in zip(x, y2, ex, ey, range(NC)):
gr.SetPoint(i, x0, y0)
print("Set point {} {},{}".format(i, x0, y0))
gr.SetPointError(i, x0e, x0e, y0e, y0e)
print("Set point Error {} {},{}".format(i, x0e, y0e))
print(gr)
return gr
Hist(100, -5, 5, color='salmon',
drawstyle='hist').FillRandom(F1('TMath::Gaus(x, 2, 1)'), 500))
stack.Add(
Hist(100, -5, 5, color='powderblue',
drawstyle='hist').FillRandom(F1('TMath::Gaus(x, 2, 0.6)'), 300))
objects.append(stack)
# create some random histograms
for i, (mu, sigma, n, c,
s) in enumerate(zip(mus, sigmas, events, colors, styles)):
hist = Hist(100,
-5,
5,
color=c,
fillstyle=s,
drawstyle='hist' if i % 2 == 0 else '')
hist.FillRandom(F1('TMath::Gaus(x,{0},{1})'.format(mu, sigma)), n)
objects.append(hist)
# create a graph
graph = Graph(10, drawstyle='P')
for i in range(10):
x = -2 + i * 4 / 10.
graph.SetPoint(i, x, 40 + 10 * sin(x))
objects.append(graph)
draw(objects, xtitle='Some Variable [Units]', ytitle='Events', ypadding=0.05)
# see rootpy.plotting.utils.get_limits for details on what arguments are
# supported for setting the axes ranges.
wait()
def plot_normalized_luminosity_ratio(detector_one_data, detector_two_data, style, run_name):
'''
:param detector_one_data: Data from one detector type, such as ATLAS' LUCID, in a list of lists, every entry is one
luminsoity block, with the luminosity block being a list of BCID data, assumed to be same
length as detector_two_data
:param detector_two_data: Data from another detector type, such as ATLAS' LUCID, in a list of lists, every entry is
one luminosity block, with the luminosity block being a list of BCID data, assumed to be same
length as detector_one_data
:param style: The ROOT style for the graph, generally 'ATLAS'
:return: ROOT plots of the ratio of luminosities over the luminosity, normalized to one
'''
# Set ROOT graph style
set_style(str(style))
print("Number of Luminosity Blocks included: " + str(len(detector_one_data)))
# Get ratio of the detectors
luminosity_ratio = []
lumi_blocks = []
for block in range(len(detector_one_data)):
for bcid in range(len(detector_one_data[block])):
detector_one_point = detector_one_data[block][bcid]
detector_two_point = detector_two_data[block][bcid]
# Check if the blocks are zero
if detector_one_point != 0.0 and detector_two_point != 0.0:
ratio = -math.log(1 - detector_one_point) / -math.log(1 - detector_two_point)
luminosity_ratio.append(ratio)
lumi_blocks.append(block)
# Normalize the ratios
def normalize(x, x_min, x_max):
top = x - x_min
bottom = x_max - x_min
return top / bottom
max_ratio = max(luminosity_ratio)
min_ratio = min(luminosity_ratio)
print("Max ratio: " + str(max_ratio))
print("Min Ratio: " + str(min_ratio))
for ratio_entry in range(len(luminosity_ratio)):
luminosity_ratio[ratio_entry] = normalize(luminosity_ratio[ratio_entry], x_max=max_ratio, x_min=min_ratio)
# create graph
graph = Graph(len(lumi_blocks), title=run_name)
for i, (xx, yy) in enumerate(zip(lumi_blocks, luminosity_ratio)):
graph.SetPoint(i, float(xx), float(yy))
# set visual attributes
graph.markercolor = 'blue'
graph.xaxis.SetTitle("Luminosity Block")
graph.yaxis.SetTitle("Luminosity [Normalized Ratio]")
graph.xaxis.SetRangeUser(min(lumi_blocks), max(lumi_blocks))
graph.yaxis.SetRangeUser(min(luminosity_ratio), max(luminosity_ratio))
# plot with ROOT
canvas = Canvas()
graph.Draw("AP")
label = ROOT.TText(0.8, 0.9, str(run_name))
label.SetTextFont(43)
label.SetTextSize(25)
label.SetNDC()
label.Draw()
canvas.Modified()
canvas.Update()
wait(True)
def plot_percent_luminosity_ratio_sum(detector_one_data, detector_two_data, style, run_name):
'''
:param detector_one_data: Data from one detector type, such as ATLAS' LUCID, in a list of lists, every entry is one
luminsoity block, with the luminosity block being a list of BCID data, assumed to be same
length as detector_two_data
:param detector_two_data: Data from another detector type, such as ATLAS' LUCID, in a list of lists, every entry is
one luminosity block, with the luminosity block being a list of BCID data, assumed to be same
length as detector_one_data
:param style: The ROOT style for the graph, generally 'ATLAS'
:return: ROOT plots of the ratio of luminosity sums over the luminosity, as percentage difference from first data point
'''
# Set ROOT graph style
set_style(str(style))
print("Number of Luminosity Blocks included: " + str(len(detector_one_data)))
# Get average value of the rate for each luminosity block
temp_detector_one = [[] for _ in xrange(len(detector_one_data))]
temp_detector_two = [[] for _ in xrange(len(detector_one_data))]
for block in range(len(detector_one_data)):
detector_one_avg = 0
one_count = 0
detector_two_avg = 0
two_count = 0
for bcid in range(len(detector_one_data[block])):
detector_one_point = detector_one_data[block][bcid]
detector_two_point = detector_two_data[block][bcid]
detector_one_avg += detector_one_point
one_count += 1
detector_two_avg += detector_two_point
two_count += 1
if one_count != 0:
detector_one_avg = detector_one_avg / one_count
detector_two_avg = detector_two_avg / two_count
temp_detector_one[block].append(detector_one_avg)
temp_detector_two[block].append(detector_two_avg)
# Reassign temp to the original lists
detector_one_data = temp_detector_one
detector_two_data = temp_detector_two
# Get ratio of the detectors
luminosity_ratio = []
lumi_blocks = []
for block in range(len(detector_one_data)):
for bcid in range(len(detector_one_data[block])):
detector_one_point = detector_one_data[block][bcid]
detector_two_point = detector_two_data[block][bcid]
# Check if the blocks are zero
if detector_one_point != 0.0 and detector_two_point != 0.0:
ratio = -math.log(1 - detector_one_point) / -math.log(1 - detector_two_point)
luminosity_ratio.append(ratio)
lumi_blocks.append(block)
# Get percentage difference based off the first block and BCID
first_point = luminosity_ratio[0]
for index in range(len(luminosity_ratio)):
luminosity_ratio[index] = (luminosity_ratio[index] / first_point) - 1
# Delete last point, when the beams are being shut down and there are massive spikes
luminosity_ratio = luminosity_ratio[:-1]
lumi_blocks = lumi_blocks[:-1]
# create graph
graph = Graph(len(lumi_blocks), title=run_name)
for i, (xx, yy) in enumerate(zip(lumi_blocks, luminosity_ratio)):
graph.SetPoint(i, float(xx), float(yy))
# set visual attributes
graph.markercolor = 'blue'
graph.xaxis.SetTitle("Luminosity Block")
graph.yaxis.SetTitle("Luminosity [Average Percent Ratio]")
graph.xaxis.SetRangeUser(min(lumi_blocks), max(lumi_blocks))
graph.yaxis.SetRangeUser(min(luminosity_ratio), max(luminosity_ratio))
# plot with ROOT
canvas = Canvas()
graph.Draw("AP")
label = ROOT.TText(0.8, 0.9, str(run_name))
label.SetTextFont(43)
label.SetTextSize(25)
label.SetNDC()
label.Draw()
canvas.Modified()
canvas.Update()
wait(True)
# remove the errorbars tmphandles = [] tmplabels = [] for a,b in zip(handles,labels): if type(a)==Line2D: continue tmphandles.append(a[0]) tmplabels.append(b) # use them in the legend axes.legend(tmphandles, tmplabels, loc='best',numpoints=1) if 'Data' in hists: # print list(stack.sum.y()) # ratioplot = Graph.divide( Graph(hists['Data']), stack.sum ) ratioplot = Graph() ratioplot.Divide( hists['Data'], stack.sum , 'pois' ) ratioplot.color = "black" if hists["Data"].Integral(): tmpyerror,tmpyerror2 = zip(*list(ratioplot.yerr()) ) tmpx = list(ratioplot.x()) tmpy = list(ratioplot.y()) tmpxy = zip(tmpx,tmpy,tmpyerror) # print tmpxy tmpxy = [tmp for tmp in tmpxy if tmp[1]!=0 ] # print tmpxy tmpx,tmpy,tmpyerror = zip(*tmpxy) # print tmpyerror axes_ratio.errorbar(tmpx, tmpy, # list(ratioplot.y()),
def draw_ratio(a, b, field, category,
textsize=22,
ratio_range=(0, 2),
ratio_line_values=[0.5, 1, 1.5],
optional_label_text=None,
normalize=True,
logy=False):
"""
Draw a canvas with two Hists normalized to unity on top
and a ratio plot between the two hist
Parameters:
- a: Nominal Hist (denominator in the ratio)
- b: Shifted Hist (numerator in the ratio)
- field: variable field (see variables.py)
- category: analysis category (see categories/*)
"""
xtitle = get_xtitle(field)
plot = RatioPlot(xtitle=xtitle,
ytitle='{0}Events'.format(
'Normalized ' if normalize else ''),
ratio_title='A / B',
ratio_limits=ratio_range,
ratio_line_values=ratio_line_values,
logy=logy)
if normalize:
a_integral = a.integral()
if a_integral != 0:
a /= a_integral
b_integral = b.integral()
if b_integral != 0:
b /= b_integral
a.title = 'A: ' + a.title
b.title = 'B: ' + b.title
a.color = 'black'
b.color = 'red'
a.legendstyle = 'L'
b.legendstyle = 'L'
a.markersize = 0
b.markersize = 0
a.linewidth = 2
b.linewidth = 2
a.fillstyle = 'hollow'
b.fillstyle = 'hollow'
a.linestyle = 'solid'
b.linestyle = 'dashed'
a.drawstyle='hist E0'
b.drawstyle='hist E0'
plot.draw('main', [a, b], ypadding=(0.3, 0.))
ratio = Hist.divide(a, b, fill_value=-1)
ratio.drawstyle = 'hist'
ratio.color = 'black'
ratio_band = Graph(ratio, fillstyle='/', fillcolor='black', linewidth=0)
ratio_band.drawstyle = '20'
plot.draw('ratio', [ratio_band, ratio])
with plot.pad('main') as pad:
# legend
# leg = Legend([a, b], 0.2, 0.2, 0.45,
# margin=0.35, textsize=textsize)
leg = Legend([a, b])
leg.Draw()
# draw the category label
if category is not None:
label = ROOT.TLatex(
pad.GetLeftMargin() + 0.04, 0.87,
category.label)
label.SetNDC()
label.SetTextFont(43)
label.SetTextSize(textsize)
label.Draw()
# show p-value and chi^2
if a.integral() != 0 and b.integral() != 0:
pvalue = a.Chi2Test(b, 'WW')
chi2 = a.Chi2Test(b, 'WW CHI2/NDF')
else:
pvalue = -9999.
chi2 = -9999.
pvalue_label = ROOT.TLatex(
pad.GetLeftMargin() + 0.04, 0.8,
"p-value={0:.2f}".format(pvalue))
pvalue_label.SetNDC(True)
pvalue_label.SetTextFont(43)
pvalue_label.SetTextSize(textsize)
pvalue_label.Draw()
chi2_label = ROOT.TLatex(
pad.GetLeftMargin() + 0.04, 0.72,
"#frac{{#chi^{{2}}}}{{ndf}}={0:.2f}".format(chi2))
chi2_label.SetNDC(True)
chi2_label.SetTextFont(43)
chi2_label.SetTextSize(textsize)
chi2_label.Draw()
if optional_label_text is not None:
optional_label = ROOT.TLatex(pad.GetLeftMargin()+0.01, 1 - pad.GetTopMargin() + 0.005,
optional_label_text )
optional_label.SetNDC(True)
optional_label.SetTextFont(43)
optional_label.SetTextSize(textsize)
optional_label.Draw()
if ATLAS_LABEL.lower() == 'internal':
x = 0.67
y = 1-pad.GetTopMargin()+0.005
else:
x = (1. - pad.GetRightMargin() - 0.03) - len(ATLAS_LABEL) * 0.025
y = 1-pad.GetTopMargin()+0.01
ATLAS_label(x, y,
sep=0.132, pad=pad, sqrts=None,
text=ATLAS_LABEL,
textsize=textsize)
return plot
def plot_multiple_all_luminosity_block_ratio(all_detector_one_data, all_detector_two_data, all_detector_three_data,
background_list, style, name):
'''
:param all_detector_one_data: A dictionary of the run name to a list of lists of luminosity blocks
:param all_detector_two_data: A dictionary of the run name to a list of lists of luminosity blocks
:param style: a string corresponding to a ROOT graphing style, e.g. 'ATLAS'
:return: Plot of all the detector data on a graph chronologically according to run number
'''
# Set ROOT graph style
set_style(str(style))
# Get average value of the rate for each luminosity block
temp_detector_one = copy.deepcopy(all_detector_one_data)
temp_detector_two = copy.deepcopy(all_detector_two_data)
temp_detector_three = copy.deepcopy(all_detector_three_data)
print(sorted(all_detector_one_data.keys()))
for run in sorted(all_detector_one_data.keys()):
block_count = 0
for block in range(len(all_detector_one_data.get(run)) - 1):
del temp_detector_one.get(run)[block][:]
del temp_detector_two.get(run)[block][:]
del temp_detector_three.get(run)[block][:]
block_count += 1
detector_one_avg = 0
one_count = 0
detector_two_avg = 0
two_count = 0
detector_three_avg = 0
three_count = 0
for bcid in range(len(all_detector_one_data.get(run)[block])):
# Gets the previous BCID luminosity to subtract as the background
if run == "286282":
detector_one_point_background = all_detector_one_data.get(run)[block][bcid - 1]
detector_two_point_background = all_detector_two_data.get(run)[block][bcid - 1]
detector_three_point_background = all_detector_three_data.get(run)[block][bcid - 1]
print("BCID [N-1]: " + str(detector_one_point_background))
print("BCID [N]: " + str(all_detector_one_data.get(run)[block][bcid]))
detector_one_point = -math.log(1 - all_detector_one_data.get(run)[block][bcid]) \
+ math.log(1 - detector_one_point_background)
detector_three_point = -math.log(1 - all_detector_three_data.get(run)[block][bcid]) \
+ math.log(1 - detector_three_point_background)
print("Detector 1 Point: " + str(detector_one_point))
detector_two_point = -math.log(1 - all_detector_two_data.get(run)[block][bcid]) \
+ math.log(1 - detector_two_point_background)
detector_one_avg += detector_one_point
one_count += 1
detector_two_avg += detector_two_point
two_count += 1
detector_three_avg += detector_three_point
three_count += 1
else:
detector_one_point = -math.log(1 - all_detector_one_data.get(run)[block][bcid])
detector_two_point = -math.log(1 - all_detector_two_data.get(run)[block][bcid])
detector_three_point = -math.log(1 - all_detector_three_data.get(run)[block][bcid])
detector_one_avg += detector_one_point
one_count += 1
detector_two_avg += detector_two_point
two_count += 1
detector_three_avg += detector_three_point
three_count += 1
if one_count != 0:
detector_one_avg = detector_one_avg / one_count
detector_two_avg = detector_two_avg / two_count
detector_three_avg = detector_three_avg / three_count
temp_detector_one.get(run)[block_count - 1].append(detector_one_avg)
temp_detector_two.get(run)[block_count - 1].append(detector_two_avg)
temp_detector_three.get(run)[block_count - 1].append(detector_three_avg)
# Remove the last luminosity block from each run, the one that generally spikes
temp_detector_one[run] = temp_detector_one[run][:-10]
temp_detector_two[run] = temp_detector_two[run][:-10]
temp_detector_three[run] = temp_detector_three[run][:-10]
# Reassign temp to the original lists
all_detector_one_data = temp_detector_one
all_detector_two_data = temp_detector_two
all_detector_three_data = temp_detector_three
# Get ratio of the detectors 1 and 2
luminosity_ratio = []
lumi_blocks = []
block_count1 = 0
for run in sorted(all_detector_one_data.keys()):
for block in range(len(all_detector_one_data.get(run))):
block_count1 += 1
for bcid in range(len(all_detector_one_data.get(run)[block])):
detector_one_point = all_detector_one_data.get(run)[block][bcid]
detector_two_point = all_detector_two_data.get(run)[block][bcid]
# Check if the blocks are zero
if detector_one_point != 0.0 and detector_two_point != 0.0:
ratio = detector_one_point / detector_two_point
luminosity_ratio.append(ratio)
lumi_blocks.append(block_count1)
# Get ratio of the detectors 1 and 3
luminosity_ratio_1 = []
lumi_blocks_1 = []
block_count2 = 0
for run in sorted(all_detector_one_data.keys()):
for block in range(len(all_detector_one_data.get(run))):
block_count2 += 1
for bcid in range(len(all_detector_one_data.get(run)[block])):
detector_one_point = all_detector_one_data.get(run)[block][bcid]
detector_three_point = all_detector_three_data.get(run)[block][bcid]
# Check if the blocks are zero
if detector_one_point != 0.0 and detector_three_point != 0.0:
ratio = detector_one_point / detector_three_point
luminosity_ratio_1.append(ratio)
lumi_blocks_1.append(block_count2)
# Get percentage difference based off the first block and BCID
first_point = luminosity_ratio[0]
first_point_1 = luminosity_ratio_1[0]
for index in range(len(luminosity_ratio)):
luminosity_ratio[index] = 100 * ((luminosity_ratio[index] / first_point) - 1)
for index in range(len(luminosity_ratio_1)):
luminosity_ratio_1[index] = 100 * ((luminosity_ratio_1[index] / first_point_1) - 1)
# create graph
graph = Graph(len(lumi_blocks))
for i, (xx, yy) in enumerate(zip(lumi_blocks, luminosity_ratio)):
graph.SetPoint(i, float(xx), float(yy))
# set visual attributes
graph.markercolor = 'blue'
graph.xaxis.SetTitle("Luminosity Block")
graph.yaxis.SetTitle("Luminosity [Average Percent Ratio]")
graph.xaxis.SetRangeUser(min(lumi_blocks), max(lumi_blocks))
graph.yaxis.SetRangeUser(min(luminosity_ratio), max(luminosity_ratio))
# plot with ROOT
canvas = Canvas()
graph.Draw("AP")
canvas.Update()
# add points from detectors 1 and 3
# create graph
graph1 = Graph(len(lumi_blocks_1))
for i, (xx, yy) in enumerate(zip(lumi_blocks_1, luminosity_ratio_1)):
graph1.SetPoint(i, float(xx), float(yy))
# set visual attributes
graph1.linecolor = 'white' # Hides the lines at this time
graph1.markercolor = 'red'
# graph1.xaxis.SetRangeUser(min(lumi_blocks_1), max(lumi_blocks_1))
# graph1.yaxis.SetRangeUser(min(luminosity_ratio_1), max(luminosity_ratio_1))
graph1.Draw("P")
canvas.Update()
# Draw lines for different runs
run_length = 0
for run in sorted(all_detector_one_data.keys()):
run_length += len(all_detector_one_data.get(run))
line = ROOT.TLine(run_length, min(luminosity_ratio),
run_length, max(luminosity_ratio))
line.Draw()
line_label = ROOT.TText(run_length - 30, max(luminosity_ratio) - 1.5, str(run))
line_label.SetTextAngle(90)
line_label.SetTextSize(18)
line_label.SetTextFont(43)
line_label.Draw()
label = ROOT.TText(0.7, 0.8, str(name))
label.SetTextFont(43)
label.SetTextSize(25)
label.SetNDC()
label.Draw()
canvas.Modified()
canvas.Update()
wait(True)
print '[%d]' % i, entry
idx = raw_input('which one should I use? ')
idx = int(idx)
_, to_use = matches[idx]
print to_use.start.str, '-->', time.str
points.append((to_use.start.time, time.time))
#linear fit
from rootpy import ROOT
ROOT.gROOT.SetBatch()
from rootpy.plotting import Graph, F1, Canvas
fcn = F1(args.fitfunc)
graph = Graph(len(points))
for idx, point in enumerate(points):
x, y = point
graph.SetPoint(idx, x, y)
graph.fit(fcn)
canvas = Canvas()
graph.Draw()
canvas.SaveAs("shifts.png")
with open(args.out, 'w') as out:
for i, entry in enumerate(entries):
entry.remap(fcn)
out.write('%d\n' % i)
out.write('%s\n\n' % entry.string)
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoMinorLocator, MultipleLocator
# set the random seed
ROOT.gRandom.SetSeed(42)
np.random.seed(42)
# points
x = np.sort(np.random.random(10)) * 3500
y = np.random.random(10)
# set style for ROOT
set_style('ATLAS')
# create graph
graph = Graph(x.shape[0])
for i, (xx, yy) in enumerate(zip(x, y)):
graph.SetPoint(i, xx, yy)
# set visual attributes
graph.linecolor = 'blue'
graph.markercolor = 'blue'
graph.xaxis.SetTitle("E_{T} [GeV]")
graph.yaxis.SetTitle("d#sigma_{jet}/dE_{T,jet} [fb/GeV]")
graph.xaxis.SetRangeUser(0, 3500)
graph.yaxis.SetRangeUser(0, 1)
# plot with ROOT
canvas = Canvas()
graph.Draw("APL")
axislabel = ""
for chunk in histogramName.split("_"):
if "SR" in chunk or "minus" in chunk:
continue
axislabel += chunk
# axislabel = " ".join(histogramName.split("_")[2:])
axislabel = axislabel.split("<")[0].split(">")[0]
if 'DataMain' in hists:
if hists["DataMain"].Integral() and plotWithMPL:
ratioplot = Graph()
ratioplot.Divide( hists['DataMain'], stack.sum , 'pois' )
# ratioplot.color = "green"
tmpyerror,tmpyerror2 = zip(*list(ratioplot.yerr()) )
tmpx = list(ratioplot.x())
tmpy = list(ratioplot.y())
tmpxy = zip(tmpx,tmpy,tmpyerror)
# print tmpxy
tmpxy = [tmp for tmp in tmpxy if tmp[1]!=0 ]
# print tmpxy
tmpx,tmpy,tmpyerror = zip(*tmpxy)
# print tmpyerror
# axes_ratio.errorbar(tmpx,tmpy, yerr = tmpyerror,xerr=False, emptybins=False, marker='o', lw=1, color="black")
axes_ratio.errorbar(tmpx, tmpy,
# list(ratioplot.y()),
yerr = tmpyerror,
# axes_ratio.set_xlabel(histogramName.replace("_"," ").replace(">","$>$").replace("<","$<$") )
# get handles
handles, labels = axes.get_legend_handles_labels()
# remove the errorbars
handles = [h[0] for h in handles]
# use them in the legend
axes.legend(handles, labels, loc='best',numpoints=1)
if 'Data' in hists:
ratioplot = Graph.divide( Graph(hists['Data']), stack.sum , 'pois' )
ratioplot.color = "black"
axes_ratio.errorbar(list(ratioplot.x()) ,
list(ratioplot.y()),
yerr=[ x[0] for x in list(ratioplot.yerr() ) ] ,
# xerr=list(ratioplot.y()),
fmt='o',
color="black")
yticks(arange(0,2.0,0.2))
ylim([0,2])
axes_ratio.set_ylabel('Data/MC')
def plot_all_integrated_luminosity(all_detector_one_data, all_detector_two_data, all_detector_three_data, block_length,
bcid_status, background_list, style, name, detector_one_calibration,
detector_two_calibration, detector_three_calibration):
'''
Take all the luminosity ratio for each luminosity block and multiply by the time to get the integrated luminosity
:param all_detector_one_data: A dictionary of the run name to a list of lists of luminosity blocks
:param all_detector_two_data: A dictionary of the run name to a list of lists of luminosity blocks
:param block_length: A dictionary the same length as the detector arrays containing the luminosity block length (time)
:param style: a string corresponding to a ROOT graphing style, e.g. 'ATLAS'
:return: Plot of all the detector data on a graph chronologically according to run number
'''
# Set ROOT graph style
set_style(str(style))
# Get average value of the rate for each luminosity block
temp_detector_one = copy.deepcopy(all_detector_one_data)
temp_detector_two = copy.deepcopy(all_detector_two_data)
temp_detector_three = copy.deepcopy(all_detector_three_data)
print(sorted(all_detector_one_data.keys()))
for run in sorted(all_detector_one_data.keys()):
block_count = 0
for block in range(len(all_detector_one_data.get(run)) - 1):
del temp_detector_one.get(run)[block][:]
del temp_detector_two.get(run)[block][:]
del temp_detector_three.get(run)[block][:]
block_count += 1
detector_one_avg = 0
one_count = 0
detector_two_avg = 0
two_count = 0
detector_three_avg = 0
three_count = 0
for bcid in range(len(all_detector_one_data.get(run)[block])):
# Gets the previous BCID luminosity to subtract as the background
if run in background_list:
if bcid_status.get(run)[block][bcid - 1] == 0:
detector_one_point_background = all_detector_one_data.get(run)[block][bcid - 1]
detector_two_point_background = all_detector_two_data.get(run)[block][bcid - 1]
detector_three_point_background = all_detector_three_data.get(run)[block][bcid - 1]
print("BCID [N-1] Stability: " + str(bcid_status.get(run)[block][bcid - 1]))
print("BCID [N] Stability: " + str(bcid_status.get(run)[block][bcid]))
detector_one_point = -math.log(1 - all_detector_one_data.get(run)[block][bcid]) \
+ math.log(1 - detector_one_point_background)
print("Detector 1 Point: " + str(detector_one_point))
detector_two_point = -math.log(1 - all_detector_two_data.get(run)[block][bcid]) \
+ math.log(1 - detector_two_point_background)
print("Detector 2 Point: " + str(detector_two_point))
detector_three_point = -math.log(1 - all_detector_three_data.get(run)[block][bcid]) \
+ math.log(1 - detector_three_point_background)
detector_one_avg += detector_one_point
one_count += 1
detector_two_avg += detector_two_point
two_count += 1
detector_three_avg += detector_three_point
three_count += 1
else:
print("No empty BCID to subtract background from")
'''
print("BCID [N] Stability: " + str(bcid_status.get(run)[block][bcid]))
detector_one_point = -math.log(1 - all_detector_one_data.get(run)[block][bcid]) #\
#+ math.log(1 - detector_one_point_background)
print("Detector 1 Point: " + str(detector_one_point))
detector_two_point = -math.log(1 - all_detector_two_data.get(run)[block][bcid]) #\
#+ math.log(1 - detector_two_point_background)
print("Detector 2 Point: " + str(detector_two_point))
detector_three_point = -math.log(1 - all_detector_three_data.get(run)[block][bcid]) #\
#+ math.log(1 - detector_three_point_background)
detector_one_avg += detector_one_point
one_count += 1
detector_two_avg += detector_two_point
two_count += 1
detector_three_avg += detector_three_point
three_count += 1
'''
if bcid_status.get(run)[block][bcid] > 0.0:
detector_one_point = -math.log(1 - all_detector_one_data.get(run)[block][bcid])
detector_two_point = -math.log(1 - all_detector_two_data.get(run)[block][bcid])
detector_three_point = -math.log(1 - all_detector_three_data.get(run)[block][bcid])
detector_one_avg += detector_one_point
one_count += 1
detector_two_avg += detector_two_point
two_count += 1
detector_three_avg += detector_three_point
three_count += 1
if one_count != 0:
detector_one_avg = detector_one_avg / one_count
detector_two_avg = detector_two_avg / two_count
detector_three_avg = detector_three_avg / three_count
temp_detector_one.get(run)[block_count - 1].append(detector_one_avg)
temp_detector_two.get(run)[block_count - 1].append(detector_two_avg)
temp_detector_three.get(run)[block_count - 1].append(detector_three_avg)
# Remove the last luminosity block from each run, the one that generally spikes
temp_detector_one[run] = temp_detector_one[run][:-10]
temp_detector_two[run] = temp_detector_two[run][:-10]
temp_detector_three[run] = temp_detector_three[run][:-10]
# Reassign temp to the original lists
all_detector_one_data = temp_detector_one
all_detector_two_data = temp_detector_two
all_detector_three_data = temp_detector_three
# Get integrated luminosity of the detectors
integrated_luminosity_one = []
integrated_luminosity_two = []
integrated_luminosity_three = []
luminosity_ratio_two = []
luminosity_ratio_three = []
lumi_blocks = []
block_count1 = 0
lumi_total = 0
lumi_total_two = 0
lumi_total_three = 0
# To keep track of how long each run actually is when plotting
run_length_dict = {}
for run in sorted(all_detector_one_data.keys()):
run_length_dict[run] = 0
for block in range(len(all_detector_one_data.get(run))):
block_count1 += 1
for bcid in range(len(all_detector_one_data.get(run)[block])):
detector_one_point = all_detector_one_data.get(run)[block][bcid]
detector_two_point = all_detector_two_data.get(run)[block][bcid]
detector_three_point = all_detector_three_data.get(run)[block][bcid]
if detector_one_point != 0.0 and detector_two_point != 0.0 and detector_three_point != 0.0:
# Use conversion factor
converted_point_one = convert_to_raw_luminosity(11.245, 20.8, detector_one_point)
converted_point_two = convert_to_raw_luminosity(11.245, 20.8, detector_two_point)
converted_point_three = convert_to_raw_luminosity(11.245, 20.8, detector_three_point)
ratio_one_two = converted_point_one / converted_point_two
ratio_one_three = converted_point_one / converted_point_three
luminosity_ratio_two.append(ratio_one_two)
luminosity_ratio_three.append(ratio_one_three)
length = block_length.get(run)[block][bcid]
lumi_total += converted_point_one * length
lumi_total_two += converted_point_two * length
lumi_total_three += converted_point_three * length
integrated_luminosity_one.append(lumi_total)
integrated_luminosity_two.append(lumi_total_two)
integrated_luminosity_three.append(lumi_total_three)
lumi_blocks.append(block_count1)
run_length_dict[run] += 1
# Get percentage difference based off the first block and BCID
first_point = detector_one_calibration / detector_two_calibration
third_point = detector_one_calibration / detector_three_calibration
for index in range(len(integrated_luminosity_one)):
luminosity_ratio_two[index] = 100 * ((luminosity_ratio_two[index] / first_point) - 1)
luminosity_ratio_three[index] = 100 * ((luminosity_ratio_three[index] / third_point) - 1)
# create graph
graph = Graph(len(integrated_luminosity_one))
for i, (xx, yy) in enumerate(zip(integrated_luminosity_one, luminosity_ratio_two)):
graph.SetPoint(i, float(xx), float(yy))
# set visual attributes
# Set temp list for the min and max functions
luminosity_ratio = luminosity_ratio_two + luminosity_ratio_three
integrated_luminosity = integrated_luminosity_one
#integrated_luminosity = lumi_blocks
graph.markercolor = 'blue'
graph.yaxis.SetTitle("Luminosity Ratio [Percent]")
graph.xaxis.SetTitle("Luminosity [Integrated]")
graph.yaxis.SetRangeUser(min(luminosity_ratio),
max(luminosity_ratio))
graph.xaxis.SetRangeUser(min(integrated_luminosity),
max(integrated_luminosity))
# plot with ROOT
canvas = Canvas()
graph.Draw("AP")
canvas.Update()
# add points from detectors 1 and 3
# create graph
graph1 = Graph(len(integrated_luminosity))
for i, (xx, yy) in enumerate(zip(integrated_luminosity, luminosity_ratio_three)):
graph1.SetPoint(i, float(xx), float(yy))
# set visual attributes
graph1.linecolor = 'white' # Hides the lines at this time
graph1.markercolor = 'red'
graph1.Draw("P")
canvas.Update()
# Draw lines for different runs
run_length = 0
total_length = 0
num_run = 0
print"Integrated Luminosity length: ",len(integrated_luminosity)
for run in sorted(all_detector_one_data.keys()):
print str(run)
total_length += run_length_dict[run]
print"Total Length: ",total_length
#run_length = integrated_luminosity[total_length - 1]
print"Run Length", run_length
line = ROOT.TLine(run_length, min(luminosity_ratio),
run_length, max(luminosity_ratio))
line.Draw()
line_label = ROOT.TText(run_length - 30, max(luminosity_ratio) - 1.5, str(run))
line_label.SetTextAngle(90)
line_label.SetTextSize(18)
line_label.SetTextFont(43)
line_label.Draw()
label = ROOT.TText(0.2, 0.9, str(name))
label.SetTextFont(43)
label.SetTextSize(25)
label.SetNDC()
label.Draw()
canvas.Modified()
canvas.Update()
wait(True)
def qqplot(h1, h2, quantiles=None):
"""
Return a Graph of a QQ plot and confidence band
"""
if quantiles is None:
quantiles = max(min(len(h1), len(h2)) / 2, 1)
nq = quantiles
xq = array('d',
[0.] * nq) # position where to compute the quantiles in [0,1]
yq1 = array('d', [0.] * nq) # array to contain the quantiles
yq2 = array('d', [0.] * nq) # array to contain the quantiles
for i in xrange(nq):
xq[i] = float(i + 1) / nq
h1.GetQuantiles(nq, yq1, xq)
h2.GetQuantiles(nq, yq2, xq)
xq_plus = array('d', [0.] * nq)
xq_minus = array('d', [0.] * nq)
yq2_plus = array('d', [0.] * nq)
yq2_minus = array('d', [0.] * nq)
"""
KS_cv: KS critical value
1.36
KS_cv = -----------
sqrt( N )
Where 1.36 is for alpha = 0.05 (confidence level 1-5%=95%, about 2 sigma)
For 1 sigma (alpha=0.32, CL=68%), the value in the nominator is 0.9561,
it is gotten by GetCriticalValue(1, 1 - 0.68).
NOTE:
* For 1-sample KS test (data and theoretic), N should be n
* For 2-sample KS test (2 data set), N should be sqrt(m*n/(m+n))! Here is the case
m or n (size of samples) should be effective size for a histogram
* Critical value here is valid for only for sample size >= 80 (some references say 35)
which means, for example, for a unweighted histogram, it must have more than 80 (or 35)
entries filled and then confidence band is reliable.
"""
esum1 = effective_sample_size(h1)
esum2 = effective_sample_size(h2)
# one sigma band
KS_cv = (critical_value(1, 1 - 0.68) / sqrt(
(esum1 * esum2) / (esum1 + esum2)))
for i in xrange(nq):
xq_plus[i] = float(xq[i] + KS_cv) # upper limit
xq_minus[i] = float(xq[i] - KS_cv) # lower limit
h2.GetQuantiles(nq, yq2_plus, xq_plus)
h2.GetQuantiles(nq, yq2_minus, xq_minus)
yq2_err_plus = array('d', [0.] * nq)
yq2_err_minus = array('d', [0.] * nq)
for i in xrange(nq):
yq2_err_plus[i] = yq2_plus[i] - yq2[i]
yq2_err_minus[i] = yq2[i] - yq2_minus[i]
#forget the last point, so number of points: (nq-1)
gr = Graph(nq - 1)
for i in xrange(nq - 1):
gr[i] = (yq1[i], yq2[i])
# confidence level band
gr.SetPointEYlow(i, yq2_err_minus[i])
gr.SetPointEYhigh(i, yq2_err_plus[i])
return gr
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 test_divide():
Graph.divide(Graph(Hist(10, 0, 1).FillRandom('gaus')),
Hist(10, 0, 1).FillRandom('gaus'), 'pois')
def main(args):
fileList = glob('/data/nawoods/lepsForSIP/*.root')
files = {int(f.split('_M')[1].split('.')[0]) : f for f in fileList}
checkers = {m : MuonSIPChecker('m={}'.format(m), [f]) for m,f in files.iteritems()}
sipRMS = Graph(len(fileList), type='errors')
sipHists = []
dxyRMS = Graph(len(fileList), type='errors')
dxyHists = []
dzRMS = Graph(len(fileList), type='errors')
dzHists = []
ipHists = []
ipErrHists = []
fracFailing = Graph(len(fileList), type='errors')
for i, m in enumerate(sorted(files.keys())):
checkers[m].processSample()
checkers[m].hists['sip'].Sumw2()
sipRMS.SetPoint(i, float(m), checkers[m].hists['sip'].GetRMS())
sipRMS.SetPointError(i, 0., checkers[m].hists['sip'].GetRMSError())
sipHists.append(checkers[m].hists['sip'])
sipHists[-1].color = getColor(m)
sipHists[-1].title = "m_{{H}} = {}".format(m)
sipHists[-1].drawstyle = 'hist'
sipHists[-1].legendstyle = 'L'
sipHists[-1].linewidth = 2
sipHists[-1].scale(1./sipHists[-1].integral())
ipHists.append(checkers[m].hists['ip'])
ipHists[-1].color = getColor(m)
ipHists[-1].title = "m_{{H}} = {}".format(m)
ipHists[-1].drawstyle = 'hist'
ipHists[-1].legendstyle = 'L'
ipHists[-1].linewidth = 2
ipHists[-1].scale(1./ipHists[-1].integral())
ipErrHists.append(checkers[m].hists['ipErr'])
ipErrHists[-1].color = getColor(m)
ipErrHists[-1].title = "m_{{H}} = {}".format(m)
ipErrHists[-1].drawstyle = 'hist'
ipErrHists[-1].legendstyle = 'L'
ipErrHists[-1].linewidth = 2
ipErrHists[-1].scale(1./ipErrHists[-1].integral())
checkers[m].hists['dxy'].Sumw2()
dxyRMS.SetPoint(i, float(m), checkers[m].hists['dxy'].GetRMS())
dxyRMS.SetPointError(i, 0., checkers[m].hists['dxy'].GetRMSError())
dxyHists.append(checkers[m].hists['dxy'])
dxyHists[-1].color = getColor(m)
dxyHists[-1].title = "m_{{H}} = {}".format(m)
dxyHists[-1].drawstyle = 'hist'
dxyHists[-1].legendstyle = 'L'
dxyHists[-1].linewidth = 2
dxyHists[-1].scale(1./dxyHists[-1].integral())
checkers[m].hists['dz'].Sumw2()
dzRMS.SetPoint(i, float(m), checkers[m].hists['dz'].GetRMS())
dzRMS.SetPointError(i, 0., checkers[m].hists['dz'].GetRMSError())
dzHists.append(checkers[m].hists['dz'])
dzHists[-1].color = getColor(m)
dzHists[-1].title = "m_{{H}} = {}".format(m)
dzHists[-1].drawstyle = 'hist'
dzHists[-1].legendstyle = 'L'
dzHists[-1].linewidth = 2
dzHists[-1].scale(1./dzHists[-1].integral())
fracFailing.SetPoint(i, float(m),
1. - float(checkers[m].nPassSIP) / checkers[m].nTot)
cSIP = Canvas(1000,1000)
draw(sipHists, cSIP, xtitle='#frac{IP_{3D}}{#sigma_{IP_{3D}}}',
ytitle='arb.')
legSIP = Legend(sipHists, cSIP, textsize=.03, entrysep=0.015, leftmargin=0.6, entryheight=0.04)
legSIP.Draw("same")
cSIP.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/sips.png')
cSIPLog = Canvas(1000,1000)
draw(sipHists, cSIPLog, xtitle='#frac{IP_{3D}}{#sigma_{IP_{3D}}}',
ytitle='arb.', logy=True)
legSIPLog = Legend(sipHists, cSIPLog, textsize=.027, entrysep=0.012, leftmargin=0.6, entryheight=0.03)
legSIPLog.Draw("same")
cSIPLog.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/sipsLog.png')
cSIPRMS = Canvas(1000, 1000)
sipRMS.color = 'b'
sipRMS.drawstyle = 'PE'
sipRMS.legendstyle = 'PE'
draw(sipRMS, cSIPRMS, xtitle="m_{H}", ytitle="RMS(SIP_{3D})", xlimits=(0.,2600.))
cSIPRMS.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/sipRMS.png')
cIP = Canvas(1000,1000)
draw(ipHists, cIP, xtitle='IP_{3D}',
ytitle='arb.')
legIP = Legend(ipHists, cIP, textsize=.03, entrysep=0.015, leftmargin=0.6, entryheight=0.04)
legIP.Draw("same")
cIP.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/ips.png')
cIPLog = Canvas(1000,1000)
draw(ipHists, cIPLog, xtitle='#frac{IP_{3D}}{#sigma_{IP_{3D}}}',
ytitle='arb.', logy=True)
legIPLog = Legend(ipHists, cIPLog, textsize=.027, entrysep=0.012, leftmargin=0.6, entryheight=0.03)
legIPLog.Draw("same")
cIPLog.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/ipsLog.png')
cIPErr = Canvas(1000,1000)
draw(ipErrHists, cIPErr, xtitle='#sigma_{IP_{3D}}',
ytitle='arb.')
legIPErr = Legend(ipErrHists, cIPErr, textsize=.03, entrysep=0.015, leftmargin=0.6, entryheight=0.04)
legIPErr.Draw("same")
cIPErr.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/ipErrs.png')
cIPErrLog = Canvas(1000,1000)
draw(ipErrHists, cIPErrLog, xtitle='#sigma_{IP_{3D}}',
ytitle='arb.', logy=True)
legIPErrLog = Legend(ipErrHists, cIPErrLog, textsize=.027, entrysep=0.012, leftmargin=0.6, entryheight=0.03)
legIPErrLog.Draw("same")
cIPErrLog.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/ipErrsLog.png')
cFail = Canvas(1000, 1000)
fracFailing.color = 'b'
fracFailing.drawstyle = 'PE'
fracFailing.legendstyle = 'PE'
draw(fracFailing, cSIPRMS, xtitle="m_{H}", ytitle="Fraction failing SIP", xlimits=(0.,2600.))
cSIPRMS.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/fracFailing.png')
cDXY = Canvas(1000,1000)
draw(dxyHists, cDXY, xtitle='#Delta_{xy}',
ytitle='arb.')
legDXY = Legend(dxyHists, cDXY, textsize=.03, entrysep=0.015, leftmargin=0.6, entryheight=0.04)
legDXY.Draw("same")
cDXY.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/dxys.png')
cDXYRMS = Canvas(1000, 1000)
dxyRMS.color = 'b'
dxyRMS.drawstyle = 'PE'
dxyRMS.legendstyle = 'PE'
draw(dxyRMS, cDXYRMS, xtitle="m_{H}", ytitle="RMS(#Delta_{xy})", xlimits=(0.,2600.))
cDXYRMS.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/dxyRMS.png')
cDZ = Canvas(1000,1000)
draw(dzHists, cDZ, xtitle='#Delta_{z}',
ytitle='arb.')
legDZ = Legend(dzHists, cDZ, textsize=.03, entrysep=0.015, leftmargin=0.6, entryheight=0.04)
legDZ.Draw("same")
cDZ.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/dzs.png')
cDZRMS = Canvas(1000, 1000)
dzRMS.color = 'b'
dzRMS.drawstyle = 'PE'
dzRMS.legendstyle = 'PE'
draw(dzRMS, cDZRMS, xtitle="m_{H}", ytitle="RMS(#Delta_{z})", xlimits=(0.,2600.))
cDZRMS.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/dzRMS.png')
def __init__(self, filename):
self.parameters = {}
sys_name = re.search('Output/(\w+)/', filename).group(1)
file_list = glob.glob(filename + '/*.txt')
# Get parameter names and corresponding line numbers
with open(file_list[1]) as f:
lines = [line.rstrip('\n') for line in f]
candidates = [[item.split()[0], i] for i, item in enumerate(lines)
if re.search(u"\+\/\-.*|Chi2", item)]
for cand in candidates:
self.parameters[cand[0]] = [cand[1], []]
print(self.parameters)
for ifile in file_list:
[iy, ipt] = re.search(r"y(\d)_pt(\d+)", ifile).groups()
iy = int(iy)
ipt = int(ipt)
with open(ifile) as ftmp:
lines = [line.rstrip('\n') for line in ftmp]
mean_pt = float(lines[0].split()[1])
for par in self.parameters:
graphs = self.parameters[par][1]
parno = self.parameters[par][0]
try:
graph = next(igraph for igraph in graphs
if igraph.name == 'g_{0}_y{1}_{2}'.format(
sys_name, iy, par))
except StopIteration:
graph = Graph(len(bins_pt) - 1,
name='g_{0}_y{1}_{2}'.format(
sys_name, iy, par),
type='asymm')
graphs.append(graph)
try:
data = lines[parno].split()
except IndexError:
continue
if par == 'Chi2/nDOF:':
par_value = float(data[1])
par_error = [0, 0]
else:
par_value = float(data[2])
if (data[3] == "+/-"):
if (data[4][0] == "("):
par_error = [
float(data[4][2:-1]),
float(data[5][:-1])
]
else:
if float(data[4]) > 0.3 * par_value:
par_error = [
0.3 * par_value, 0.3 * par_value
]
else:
par_error = [
float(data[4]),
float(data[4])
]
else:
par_error = [999, 999]
graph[ipt] = (mean_pt, par_value)
graph[ipt].x.error_hi = bins_pt[ipt + 1] - mean_pt
graph[ipt].x.error_low = -bins_pt[ipt] + mean_pt
graph[ipt].y.error_low = par_error[0]
graph[ipt].y.error_hi = par_error[1]
for par in self.parameters:
graphs = self.parameters[par][1]
for graph, color in zip(graphs, colors):
graph.color = color
'mmmm' : lambda row: getattr(row, 'm3_m4_%s'%massVar),
}
def selectLowMass(row):
return row.MassDREtFSR < 110.
for ana in ['full', 'z4l']:
if ana == 'z4l':
selector = selectLowMass
else:
selector = lambda *args: True
g = {}
for ch in plotter.channels:
g[ch] = Graph(plotter.ntuples['data']['data'][ch].GetEntries(), title=titles[ch])
g[ch].color = colors[ch]
g[ch].markerstyle = markers[ch]
g[ch].drawstyle = 'P'
g[ch].SetMarkerSize(g[ch].GetMarkerSize()*1.5)
if ch == 'mmmm':
g[ch].SetMarkerSize(g[ch].GetMarkerSize()*1.18)
for ch in plotter.channels:
#nWithFSR = 0
for i, row in enumerate(plotter.ntuples['data']['data'][ch]):
if selector(row):
g[ch].SetPoint(i, getMZ1[ch](row), getMZ2[ch](row))
#if row.Mass != row.MassDREtFSR:
# nWithFSR += 1
#print "%s: %d / %d"%(ch, nWithFSR, int(plotter.ntuples['data']['data'][ch].GetEntries()))
def main():
print('Number of arguments: ', len(sys.argv), 'arguments.')
print('Argument list:', str(sys.argv))
filename = sys.argv[1]
if len(sys.argv) > 2:
fileData = sys.argv[2]
else:
fileData = None
print("Input file: ")
print(filename)
Njets = 9
if fileData is not None:
fD = root_open(fileData, 'read')
iS = 0
gGausRMSData = fD.Get("gGausRMS{:02d}".format(iS))
gGausRMSerrData = fD.Get("gGausRMS{:02d}_Systematics".format(iS))
gGausYieldData = fD.Get("gGausYield{:02d}".format(iS))
gGausYielderrData = fD.Get("gGausYield{:02d}_Systematics".format(iS))
gGammaRMSData = fD.Get("gGammaRMS{:02d}".format(iS))
gGammaRMSerrData = fD.Get("gGammaRMS{:02d}_Systematics".format(iS))
gGammaYieldData = fD.Get("gGammaYield{:02d}".format(iS))
gGammaYielderrData = fD.Get("gGammaYield{:02d}_Systematics".format(iS))
with root_open(filename, 'read') as f:
FullJets_jT = []
colors = (1, 2, 3)
for iF, c in zip((8, 6, 7), colors):
jT = [
f.get(
'AliJJetJtTask/AliJJetJtHistManager/JetConeJtWeightBin/JetConeJtWeightBinNFin{:02d}JetPt{:02d}'
.format(iF, ij)) for ij in range(Njets)
]
bgJt = [
f.get(
'AliJJetJtTask/AliJJetJtHistManager/BgJtWeightBin/BgJtWeightBinNFin{:02d}JetPt{:02d}'
.format(iF, ij)) for ij in range(Njets)
]
jetPtBin = [
f.get(
'AliJJetJtTask/AliJJetJtHistManager/JetPtBin/JetPtBinNFin{:02d}JetPt{:02d}'
.format(iF, ij)) for ij in range(Njets)
]
nJets = [h.Integral() for h in jetPtBin]
nBgs = [
f.get(
'AliJJetJtTask/AliJJetJtHistManager/BgTrkNumberBin/BgTrkNumberBinNFin{:02d}JetPt{:02d}'
.format(iF, ij)).Integral() for ij in range(Njets)
]
FullJets_jT.append(jT)
#FullJets_bgJt.append(bgJt)
#FullJets_jetPtBin.append(jetPtBin)
for j, b, nj, nb in zip(jT, bgJt, nJets, nBgs):
j.Rebin(2)
b.Rebin(2)
j.Scale(1.0 / nj, "width")
b.Scale(1.0 / nb, "width")
j.SetMarkerColor(c)
b.SetMarkerColor(c)
j.Add(b, -1.0)
jetPt = [
(int(
re.search(r'p_{T,jet} : ([\d]*)\.[\d] - ([\d]*).[\d]*',
h.GetTitle(), re.M | re.I).group(1)),
int(
re.search(r'p_{T,jet} : ([\d]*)\.[\d] - ([\d]*).[\d]*',
h.GetTitle(), re.M | re.I).group(2)))
for h in FullJets_jT[0]
] #Use regular expressions to extract jet pT range from histogram titles
jetPtCenter = array('d', [(a + b) / 2.0 for a, b in jetPt])
jetPtErrors = array('d', [(b - a) / 2.0 for a, b in jetPt])
print(jetPt, type(jetPt))
print(jetPtCenter, type(jetPtCenter))
print(jetPtErrors, type(jetPtErrors))
FullJets_fit = []
FullJets_parameters = []
FullJets_gausRMS = []
FullJets_gammaRMS = []
FullJets_gausYield = []
FullJets_gammaYield = []
for jT in FullJets_jT:
gausRMS = []
gammaRMS = []
gausRMSe = []
gammaRMSe = []
gausYield = []
gammaYield = []
gausYielde = []
gammaYielde = []
fits = []
parameters = []
for h, i in zip(jT, range(Njets)):
fit, d = fitJtHisto(h, '', 1, i, 8)
fits.append(fit)
parameters.append(d)
gausRMS.append(d['gausRMS'])
gausRMSe.append(d['gausRMSe'])
gammaRMS.append(d['gammaRMS'])
gammaRMSe.append(d['gammaRMSe'])
gausYield.append(d['gausYield'])
gausYielde.append(d['gausYielde'])
gammaYield.append(d['gammaYield'])
gammaYielde.append(d['gammaYielde'])
gausRMSg = Graph(len(gausRMS) - 2)
gammaRMSg = Graph(len(gammaRMS) - 2)
gausYieldg = Graph(len(gausYield) - 2)
gammaYieldg = Graph(len(gammaYield) - 2)
for h, he, g in zip((gausYield, gammaYield),
(gausYielde, gammaYielde),
(gausYieldg, gammaYieldg)):
for x, xe, a, e, i in zip(jetPtCenter[2:],
jetPtErrors[2:], h[2:], he[2:],
range(len(gausRMS) - 2)):
g.SetPoint(i, x, a)
g.SetPointError(i, xe, xe, e, e)
for a, b, c, d, e, f, i in zip(gausRMS[2:], gammaRMS[2:],
gausRMSe[2:], gammaRMSe[2:],
jetPtCenter[2:], jetPtErrors[2:],
range(len(gausRMS) - 2)):
gausRMSg.SetPoint(i, e, a)
gausRMSg.SetPointError(i, f, f, c, c)
gammaRMSg.SetPoint(i, e, b)
gammaRMSg.SetPointError(i, f, f, d, d)
FullJets_gausRMS.append(gausRMSg)
FullJets_gammaRMS.append(gammaRMSg)
FullJets_gausYield.append(gausYieldg)
FullJets_gammaYield.append(gammaYieldg)
FullJets_fit.append(fits)
FullJets_parameters.append(parameters)
if fileData is not None:
FullJets_gausRMS.append(gGausRMSData)
FullJets_gammaRMS.append(gGammaRMSData)
FullJets_gausYield.append(gGausYieldData)
FullJets_gammaYield.append(gGammaYieldData)
fig, axs = plt.subplots(2, 1, figsize=(7, 7))
ax = axs[0]
ax.set_xlim([0.1, 15])
ax.set_ylim([5e-6, 2e3])
ax.set_xlabel(r'$j_{T}\left(GeV/c\right)$', fontsize=labelsize)
ax.set_ylabel(r'$\frac{1}{N_{jets}}\frac{dN}{j_{T}dj_{T}}$',
fontsize=labelsize)
ratios = []
ax.set_xscale('log')
ax.set_yscale('log')
for h, c, R in zip(FullJets_jT, (0, 1, 2, 3), Rs):
h[6].SetMarkerColor(colors[c])
h[6].SetMarkerStyle(styles[c])
h[6].SetLineColor(colors[c])
ratio = h[6].Clone()
ratio.Divide(FullJets_jT[1][6])
ratios.append(ratio)
plot = rplt.errorbar(h[6],
xerr=False,
emptybins=False,
label='R = {:.1f}'.format(R),
axes=ax,
fmt='+')
line = plot.get_children()[0]
line.set_markersize(mSize)
if (styles[c] > 23):
line.set_markerfacecolor('none')
#line.set_markeredgecolor(color)
line.set_color(colors[c])
ax.text(
0.15,
0.001,
'Pythia \n Full Jets\n' + r'Anti-$k_T$' + '\n' +
r'$p_{{T,\mathrm{{jet}}}}$: {:02d}-{:02d} GeV/c'.format(60, 80),
fontsize=10)
handles, labels = ax.get_legend_handles_labels()
handles = [
container.ErrorbarContainer(h, has_xerr=False, has_yerr=True)
if isinstance(h, container.ErrorbarContainer) else h
for h in handles
]
ax.legend(handles,
labels,
loc='upper right',
numpoints=1,
prop={'family': 'monospace'})
#ax.legend(loc = 'upper right')
ax.set_xlim([0.1, 15])
ax.set_ylim([5e-6, 2e3])
ax.grid(True)
ax = axs[1]
ax.grid(True)
ax.set_xlabel(r'$j_{T}\left[GeV\right]$', fontsize=labelsize)
ax.set_ylabel('Ratio',
fontsize=labelsize) #Add x-axis labels for bottom row
for ratio, c in zip(ratios, (0, 1, 2, 3)):
#ratio.SetMarkerColor(c)
#ratio.SetMarkerStyle(24)
plot = rplt.errorbar(ratio, xerr=False, emptybins=False, axes=ax)
line = plot.get_children()[0]
line.set_markersize(mSize)
if (styles[c] > 23):
line.set_markerfacecolor('none')
#line.set_markeredgecolor(color)
line.set_color(colors[c])
ax.set_xlim([0.1, 15])
ax.set_ylim([0.1, 3])
ax.set_xscale('log')
plt.tight_layout()
plt.subplots_adjust(wspace=0,
hspace=0) #Set space between subfigures to 0
plt.savefig("PythonFigures/RcomparisonSignalPt6080.pdf".format(file),
format='pdf') #Save figure
plt.show() #Draw figure on screen
drawWithErrors2Combined(FullJets_gausRMS,
FullJets_gammaRMS,
15,
500,
1,
0,
1.65,
0,
r'jet $p_T (GeV/c)$',
r'$\sqrt{\left<j_T^2\right>}$',
'Pythia',
'PythonFigures/RcomparisonRMS',
separate=True)
return
drawWithErrors2Combined(
FullJets_gausYield,
FullJets_gammaYield,
15,
500,
1,
0,
10,
0,
r'jet $p_T$',
r'Yield',
'Pythia',
'PythonFigures/RcomparisonYield',
)
ratios = []
for hists in FullJets_jT:
if hists is not None:
ratio = []
for h, true in zip(hists, FullJets_jT[1]):
h2 = h.Clone()
h2.Divide(true)
ratio.append(h2)
else:
ratio = None
ratios.append(ratio)
fig, axs = defs.makegrid(4,
2,
xlog=True,
ylog=False,
d=d,
shareY=False)
axs = axs.reshape(8)
axs[4].set_ylabel("Ratio to R = 0.4", fontsize=labelsize)
axs[7].set_ylabel("Ratio to R = 0.4", fontsize=labelsize)
axs[1].text(
0.02,
0.005, 'Pythia\n'
r'pPb $\sqrt{s_{NN}} = 5.02 \mathrm{TeV}$'
'\n Full jets \n'
r'Anti-$k_T$',
fontsize=7
) #Add text to second subfigure, first parameters are coordinates in the drawn scale/units
for hists, R in zip(FullJets_jT, Rs):
for jT, ax, i, pT in zip(hists[2:], axs[0:4], range(0, 9),
jetPt[2:]):
rplt.errorbar(jT,
emptybins=False,
xerr=False,
label="R = {:.1f}".format(R),
axes=ax,
fmt='o') #Plot jT histogram,
ax.text(
0.3, 1e2, r'$p_{{T,\mathrm{{jet}}}}$:'
'\n'
r' {:02d}-{:02d} GeV'.format(pT[0], pT[1]))
ax.set_xlim([0.01, 20]) #Set x-axis limits
ax.set_ylim([5e-4, 2e3]) #Set y-axis limits
ax.set_yscale('log')
ax.grid(True)
for ratio in ratios:
for r, ax in zip(ratio[2:], axs[4:8]):
rplt.errorbar(r, axes=ax, emptybins=False, xerr=False)
ax.set_xlim([0.01, 20])
ax.set_ylim([0.1, 3])
ax.grid(True)
axs[0].legend(loc='lower left')
plt.savefig("PythonFigures/RcomparisonSignal.pdf",
format='pdf') #Save figure
plt.show() #Draw figure on screen
def tau_from_scan( unfoldingObject, regularisation_settings ):
variable = regularisation_settings.variable
# Plots that get outputted by the scan
lCurve = TGraph()
scanResult = TSpline3()
d = 'signal'
a = ''
# Parameters of scan
# Number of points to scan, and min/max tau
nScan = 200
minTau = 1.E-6
maxTau = 1.E-0
if variable == 'abs_lepton_eta':
minTau = 1.E-8
maxTau = 1.E-3
elif variable == 'lepton_pt':
minTau = 1.E-6
maxTau = 1.E-2
elif variable == 'NJets':
minTau = 1.E-6
maxTau = 1.E-2
# Scan is performed here
iBest = unfoldingObject.ScanTau(nScan, minTau, maxTau, scanResult, TUnfoldDensity.kEScanTauRhoSquareAvg);
# Plot the scan result
# Correlation as function of log tau
canvas = TCanvas()
# Add point corresponding to optimum tau
t = Double(0)
x = Double(0)
scanResult.GetKnot(iBest,t,x);
bestTau = Graph(1)
bestTau.SetPoint(1,t,x)
bestTau.markercolor = 'red'
bestTau.SetMarkerSize(1.5)
bestTau.SetMarkerStyle(34)
bestTau.GetXaxis().SetTitle('log(#tau)')
bestTau.GetYaxis().SetTitle('Average global correlation coefficient squared')
bestTau.SetTitle('{0} {1}'.format(variable, regularisation_settings.channel))
bestTau.GetYaxis().SetRangeUser(x*0.8,0.95)
bestTau.GetXaxis().SetLimits(log(minTau, 10), log(maxTau, 10))
bestTau.Draw('AP')
scanResult.SetMarkerColor(600)
scanResult.SetMarkerSize(0.5)
scanResult.SetMarkerStyle(20)
scanResult.Draw('LPSAME')
# Redraw to get it to appear on top of TSpline3...
bestTau.Draw('PSAME')
# Write to file
output_dir = regularisation_settings.output_folder
make_folder_if_not_exists(output_dir)
canvas.SaveAs(output_dir + '/{0}_{1}.png'.format(variable, regularisation_settings.channel) )
return unfoldingObject.GetTau()
def plot_all_luminosity_block_ratio(all_detector_one_data, all_detector_two_data, background_list, status_data, style, name,
detector_one_calibration, detector_two_calibration):
'''
:param all_detector_one_data: A dictionary of the run name to a list of lists of luminosity blocks
:param all_detector_two_data: A dictionary of the run name to a list of lists of luminosity blocks
:param style: a string corresponding to a ROOT graphing style, e.g. 'ATLAS'
:return: Plot of all the detector data on a graph chronologically according to run number
'''
# Set ROOT graph style
set_style(str(style))
# Get average value of the rate for each luminosity block
temp_detector_one = copy.deepcopy(all_detector_one_data)
temp_detector_two = copy.deepcopy(all_detector_two_data)
# print(sorted(all_detector_one_data.keys()))
for run in sorted(all_detector_one_data.keys()):
block_count = 0
print"Starting run", run
for block in range(len(all_detector_one_data.get(run)) - 1):
del temp_detector_one.get(run)[block][:]
del temp_detector_two.get(run)[block][:]
block_count += 1
detector_one_avg = 0
one_count = 0
detector_two_avg = 0
two_count = 0
for bcid in range(len(all_detector_one_data.get(run)[block])):
# Gets the previous BCID luminosity to subtract as the background
if run in background_list:
if status_data.get(run)[block][bcid - 1] <= 0.0:
detector_one_point_background = all_detector_one_data.get(run)[block][bcid - 1]
detector_two_point_background = all_detector_two_data.get(run)[block][bcid - 1]
print("BCID [N-1] Stability: " + str(status_data.get(run)[block][bcid - 1]))
else:
print("No empty BCID to subtract background from")
if all_detector_one_data.get(run)[block][bcid] < 1 and all_detector_two_data.get(run)[block][bcid] < 1:
print("BCID [N] Stability: " + str(status_data.get(run)[block][bcid]))
detector_one_point = -math.log(1 - all_detector_one_data.get(run)[block][bcid]) \
+ math.log(1 - detector_one_point_background)
print("Detector 1 Point: " + str(detector_one_point))
detector_two_point = -math.log(1 - all_detector_two_data.get(run)[block][bcid]) \
+ math.log(1 - detector_two_point_background)
print("Detector 2 Point: " + str(detector_two_point))
detector_one_avg += detector_one_point
one_count += 1
detector_two_avg += detector_two_point
two_count += 1
else:
#print(" RUN: " + str(run) + " END")
#print(all_detector_one_data.get(run)[block][bcid])
# Checking if the status is stable
if 1 > all_detector_one_data.get(run)[block][bcid] > 0.0 and 1 > all_detector_two_data.get(run)[block][bcid] > 0.0\
and status_data.get(run)[block][bcid] > 0.0:
#print("Value of Block, BCID: " + str(block) + " " + str(bcid) + " " + str(all_detector_one_data.get(run)[block][bcid]))
detector_one_point = -math.log(1 - all_detector_one_data.get(run)[block][bcid])
detector_two_point = -math.log(1 - all_detector_two_data.get(run)[block][bcid])
detector_one_avg += detector_one_point
one_count += 1
detector_two_avg += detector_two_point
two_count += 1
if one_count != 0:
detector_one_avg = detector_one_avg / one_count
detector_two_avg = detector_two_avg / two_count
if run == "286282":
print("One Average: " + str(detector_one_avg))
temp_detector_one.get(run)[block_count - 1].append(detector_one_avg)
temp_detector_two.get(run)[block_count - 1].append(detector_two_avg)
# Remove the last luminosity block from each run, the one that generally spikes
temp_detector_one[run] = temp_detector_one[run][:-10]
temp_detector_two[run] = temp_detector_two[run][:-10]
# Reassign temp to the original lists
all_detector_one_data = temp_detector_one
all_detector_two_data = temp_detector_two
# Get ratio of the detectors
luminosity_ratio = []
lumi_blocks = []
block_count1 = 0
for run in sorted(all_detector_one_data.keys()):
for block in range(len(all_detector_one_data.get(run))):
block_count1 += 1
for bcid in range(len(all_detector_one_data.get(run)[block])):
detector_one_point = all_detector_one_data.get(run)[block][bcid]
detector_two_point = all_detector_two_data.get(run)[block][bcid]
# Check if the blocks are zero
if detector_one_point != 0.0 or detector_two_point != 0.0:
ratio = detector_one_point / detector_two_point
luminosity_ratio.append(ratio)
lumi_blocks.append(block_count1)
else:
print("Run", str(run), " Block:", str(block), "One:", str(detector_one_point),
"Two:", str(detector_two_point))
print("Length lumi_blocks: " + str(len(lumi_blocks)))
print("length lumi_ratio: " + str((len(luminosity_ratio))))
# Get percentage difference based off the first block and BCID
first_point = detector_one_calibration / detector_two_calibration
for index in range(len(luminosity_ratio)):
luminosity_ratio[index] = 100 * ((luminosity_ratio[index] / first_point) - 1)
# create graph
graph = Graph(len(lumi_blocks))
for i, (xx, yy) in enumerate(zip(lumi_blocks, luminosity_ratio)):
#print (xx, yy)
graph.SetPoint(i, float(xx), float(yy))
# set visual attributes
graph.markercolor = 'blue'
graph.xaxis.SetTitle("Luminosity Block")
graph.yaxis.SetTitle("Luminosity [Average Percent Ratio]")
graph.xaxis.SetRangeUser(min(lumi_blocks), max(lumi_blocks))
#graph.yaxis.SetRangeUser(min(luminosity_ratio), max(luminosity_ratio))
graph.yaxis.SetRangeUser(-5, 5)
# plot with ROOT
canvas = Canvas()
graph.Draw("AP")
# Draw lines for different runs
run_length = 0
for run in sorted(all_detector_one_data.keys()):
#print("Length of Run " + str(run) + ": " + str(len(all_detector_one_data.get(run))))
run_length += len(all_detector_one_data.get(run))
line = ROOT.TLine(run_length, -5, #min(luminosity_ratio),
run_length, 5,) # max(luminosity_ratio))
line.Draw()
line_label = ROOT.TText(run_length - 30, 5 -1.5, str(run)) #max(luminosity_ratio) - 1.5, str(run))
line_label.SetTextAngle(90)
line_label.SetTextSize(18)
line_label.SetTextFont(43)
line_label.Draw()
label = ROOT.TText(0.7, 0.8, str(name))
label.SetTextFont(43)
label.SetTextSize(25)
label.SetNDC()
label.Draw()
canvas.Modified()
canvas.Update()
wait(True)
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoMinorLocator, MultipleLocator
# set the random seed
ROOT.gRandom.SetSeed(42)
np.random.seed(42)
# points
x = np.sort(np.random.random(10)) * 3500
y = np.random.random(10)
# set style for ROOT
set_style('ATLAS')
# create graph
graph = Graph(x.shape[0])
for i, (xx, yy) in enumerate(zip(x, y)):
graph.SetPoint(i, xx, yy)
# set visual attributes
graph.linecolor = 'blue'
graph.markercolor = 'blue'
graph.markerstyle = 'diamond'
graph.xaxis.SetTitle("E_{T} [GeV]")
graph.yaxis.SetTitle("d#sigma_{jet}/dE_{T,jet} [fb/GeV]")
graph.xaxis.SetRangeUser(0, 3500)
graph.yaxis.SetRangeUser(0, 1)
# plot with ROOT
canvas = Canvas()
graph.Draw("APL")