def test_init():
# constructor arguments are repetitions of #bins, left bound, right bound.
h2d = Hist2D(10, 0, 1, 50, -40, 10, name='2d hist')
h3d = Hist3D(3, -1, 4, 10, -1000, -200, 2, 0, 1, name='3d hist')
# variable-width bins may be created by passing the bin edges directly:
h1d_variable = Hist([1, 4, 10, 100])
h2d_variable = Hist2D([2, 4, 7, 100, 200], [-100, -50, 0, 10, 20])
h3d_variable = Hist3D([1, 3, 10], [20, 50, 100], [-10, -5, 10, 20])
# variable-width and constant-width bins can be mixed:
h2d_mixed = Hist2D([2, 10, 30], 10, 1, 5)
def events2th3(inputs, values, binsx, binsy, binsz, titlex='', titley='', titlez=''):
# Histo used to fill values
histo = Hist3D(*(binsx+binsy+binsz))
# Histo used to count entries
histo_count = Hist3D(*(binsx+binsy+binsz))
histo.SetXTitle(titlex)
histo.SetYTitle(titley)
histo.SetZTitle(titlez)
fill_hist(histo, inputs, values)
fill_hist(histo_count, inputs)
# Compute mean values in bins
histo.Divide(histo_count)
return histo
def store(regressor, name, inputs, outputfile):
# Save scikit-learn regression object
result_dir = outputfile.GetName().replace('.root', '')
if not os.path.exists(result_dir): os.mkdir(result_dir)
joblib.dump(regressor, result_dir + '/' + name + '.pkl')
# Save result in ROOT histograms if possible
if len(inputs) != 2 and len(inputs) != 3:
print 'The regression result will not be stored in a ROOT histogram. Only 2D or 3D histograms can be stored for the moment.'
return
for input in inputs:
if not input in binning:
print 'Binning is not defined for variable ' + input + '. Please add it in quantile_regression.binning if you want to store results in histograms'
return
if len(inputs) == 2:
histo = function2th2(regressor.predict, binning[inputs[0]],
binning[inputs[1]])
elif len(inputs) == 3:
histo = Hist3D(*(binning[inputs[0]] + binning[inputs[1]] +
binning[inputs[2]]),
name=name)
histo.SetXTitle(inputs[0])
histo.SetYTitle(inputs[1])
histo.SetZTitle(inputs[2])
for bx in histo.bins_range(0):
x = histo.GetXaxis().GetBinCenter(bx)
for by in histo.bins_range(1):
y = histo.GetYaxis().GetBinCenter(by)
for bz in histo.bins_range(2):
z = histo.GetZaxis().GetBinCenter(bz)
histo[bx, by, bz].value = regressor.predict([[x, y, z]])
outputfile.cd()
histo.Write()
def test_overflow_underflow():
h1d = Hist(10, 0, 1)
h1d.Fill(-1)
h1d.Fill(2)
assert_equal(h1d.underflow(), 1)
assert_equal(h1d.overflow(), 1)
h2d = Hist2D(10, 0, 1, 10, 0, 1)
h2d.Fill(-1, .5)
h2d.Fill(2, .5)
assert_equal(h2d.underflow()[h2d.axis(1).FindBin(.5)], 1)
assert_equal(h2d.overflow()[h2d.axis(1).FindBin(.5)], 1)
h2d.Fill(.5, -1)
h2d.Fill(.5, 2)
assert_equal(h2d.underflow(axis=1)[h2d.axis(0).FindBin(.5)], 1)
assert_equal(h2d.overflow(axis=1)[h2d.axis(0).FindBin(.5)], 1)
h3d = Hist3D(10, 0, 1, 10, 0, 1, 10, 0, 1)
h3d.Fill(-1, .5, .5)
h3d.Fill(2, .5, .5)
assert_equal(h3d.underflow()[h3d.axis(1).FindBin(.5)][h3d.axis(2).FindBin(.5)], 1)
assert_equal(h3d.overflow()[h3d.axis(1).FindBin(.5)][h3d.axis(2).FindBin(.5)], 1)
h3d.Fill(.5, -1, .5)
h3d.Fill(.5, 2, .5)
assert_equal(h3d.underflow(axis=1)[h3d.axis(0).FindBin(.5)][h3d.axis(2).FindBin(.5)], 1)
assert_equal(h3d.overflow(axis=1)[h3d.axis(0).FindBin(.5)][h3d.axis(2).FindBin(.5)], 1)
h3d.Fill(.5, .5, -1)
h3d.Fill(.5, .5, 2)
assert_equal(h3d.underflow(axis=2)[h3d.axis(0).FindBin(.5)][h3d.axis(1).FindBin(.5)], 1)
assert_equal(h3d.overflow(axis=2)[h3d.axis(0).FindBin(.5)][h3d.axis(1).FindBin(.5)], 1)
def test_draw():
with root_open(FILE_PATHS[0]) as f:
tree = f.tree
tree.draw('a_x')
tree.draw('a_x:a_y')
tree.draw('a_x:TMath::Exp(a_y)')
tree.draw('a_x:a_y:a_z')
tree.draw('a_x:a_y:a_z:b_x')
tree.draw('a_x:a_y:a_z:b_x:b_y', options='para')
h1 = Hist(10, -1, 2, name='h1')
h2 = Hist2D(10, -1, 2, 10, -1, 2)
h3 = Hist3D(10, -1, 2, 10, -1, 2, 10, -1, 2)
# dimensionality does not match
assert_raises(TypeError, tree.draw, 'a_x:a_y', hist=h1)
# name does not match
assert_raises(ValueError, tree.draw, 'a_x>>+something', hist=h1)
# hist is not a TH1
assert_raises(TypeError, tree.draw, 'a_x:a_y', hist=ROOT.TGraph())
# name does match and is fine (just redundant)
tree.draw('a_x>>h1', hist=h1)
assert_equal(h1.Integral() > 0, True)
h1.Reset()
tree.draw('a_x>>+h1', hist=h1)
assert_equal(h1.Integral() > 0, True)
h1.Reset()
# both binning and hist are specified
assert_raises(ValueError, tree.draw, 'a_x>>+h1(10, 0, 1)', hist=h1)
tree.draw('a_x', hist=h1)
assert_equal(h1.Integral() > 0, True)
tree.draw('a_x:a_y', hist=h2)
assert_equal(h2.Integral() > 0, True)
tree.draw('a_x:a_y:a_z', hist=h3)
assert_equal(h3.Integral() > 0, True)
h3.Reset()
tree.draw('a_x>0:a_y/2:a_z*2', hist=h3)
assert_equal(h3.Integral() > 0, True)
# create a histogram
hist = tree.draw('a_x:a_y:a_z', create_hist=True)
assert_equal(hist.Integral() > 0, True)
hist = tree.draw('a_x:a_y:a_z>>new_hist_1')
assert_equal(hist.Integral() > 0, True)
assert_equal(hist.name, 'new_hist_1')
# create_hist=True is redundant here
hist = tree.draw('a_x:a_y:a_z>>new_hist_2', create_hist=True)
assert_equal(hist.Integral() > 0, True)
assert_equal(hist.name, 'new_hist_2')
def test_bounds():
h = Hist(10, 0, 1)
assert_equal(h.bounds(), (0, 1))
h = Hist2D(10, 0, 1, 10, 1, 2)
assert_equal(h.bounds(axis=0), (0, 1))
assert_equal(h.bounds(axis=1), (1, 2))
h = Hist3D(10, 0, 1, 10, 1, 2, 10, 2, 3)
assert_equal(h.bounds(axis=0), (0, 1))
assert_equal(h.bounds(axis=1), (1, 2))
assert_equal(h.bounds(axis=2), (2, 3))
def test_quantiles():
h3d = Hist3D(10, 0, 1, 10, 0, 1, 10, 0, 1)
h3d.FillRandom('gaus')
h3d.quantiles(2)
h3d.quantiles(2, axis=1)
h3d.quantiles([0, .5, 1], axis=2)
h2d = Hist2D(100, 0, 1, 100, 0, 1)
h2d.FillRandom(F2('x+y'))
h2d.quantiles(4, axis=0)
h2d.quantiles(4, axis=1)
def test_merge_bins():
h1d = Hist(10, 0, 1)
h1d.FillRandom('gaus', 1000)
h1d_merged = h1d.merge_bins([(0, -1)])
assert_equal(h1d_merged.nbins(0), 1)
h3d = Hist3D(10, 0, 1, 10, 0, 1, 10, 0, 1)
h3d.FillRandom('gaus')
h3d_merged = h3d.merge_bins([(1, 3), (-4, -2)], axis=1)
assert_equal(h3d.GetEntries(), h3d_merged.GetEntries())
assert_equal(h3d.GetSumOfWeights(), h3d_merged.GetSumOfWeights())
assert_equal(h3d_merged.nbins(1), 6)
def test_rebinning():
h1d = Hist(100, 0, 1)
h1d.FillRandom('gaus')
assert_equal(h1d.rebinned(2).nbins(0), 50)
assert_equal(h1d.rebinned((2, )).nbins(0), 50)
assert_equal(h1d.rebinned([0, .5, 1]).nbins(0), 2)
h3d = Hist3D(10, 0, 1, 10, 0, 1, 10, 0, 1)
h3d.FillRandom('gaus')
assert_equal(h3d.rebinned(2).nbins(0), 5)
new = h3d.rebinned((2, 5, 1))
assert_equal(new.nbins(0), 5)
assert_equal(new.nbins(1), 2)
assert_equal(new.nbins(2), 10)
new = h3d.rebinned([0, 5, 10], axis=1)
assert_equal(new.nbins(1), 2)
def test_cuts(self):
with root_open(self.file_paths[0]) as f:
tree = f.tree
h1 = Hist(10, -1, 2)
h2 = Hist2D(10, -1, 2, 10, -1, 2)
h3 = Hist3D(10, -1, 2, 10, -1, 2, 10, -1, 2)
tree.draw('a_x', hist=h1)
assert_equals(h1.Integral() > 0, True)
tree.draw('a_x:a_y', hist=h2)
assert_equals(h2.Integral() > 0, True)
tree.draw('a_x:a_y:a_z', hist=h3)
assert_equals(h3.Integral() > 0, True)
h3.Reset()
tree.draw('a_x>0:a_y/2:a_z*2', hist=h3)
assert_equals(h3.Integral() > 0, True)
def test_merge_bins():
h1d = Hist(10, 0, 1)
h1d.FillRandom('gaus', 1000)
h1d_merged = h1d.merge_bins([(0, -1)])
assert_equal(h1d_merged.nbins(0), 1)
h3d = Hist3D(10, 0, 1, 10, 0, 1, 10, 0, 1)
h3d.FillRandom('gaus')
h3d_merged = h3d.merge_bins([(1, 3), (-4, -2)], axis=1)
assert_equal(h3d.GetEntries(), h3d_merged.GetEntries())
assert_equal(h3d.GetSumOfWeights(), h3d_merged.GetSumOfWeights())
assert_equal(h3d_merged.nbins(1), 6)
h = Hist(4, 0, 1)
h.Fill(-1)
h.Fill(2)
h.Fill(0.8, 2)
h.Fill(0.5, 1)
# invalid ranges
assert_raises(ValueError, h.merge_bins, [(0, 0)])
assert_raises(ValueError, h.merge_bins, [(0, 1, 2)])
assert_raises(ValueError, h.merge_bins, [(2, 1)])
assert_raises(ValueError, h.merge_bins, [(-2, 1)])
# overlapping windows
assert_raises(ValueError, h.merge_bins, [(0, 1), (1, 2)])
assert_equal(list(h.y(overflow=True)), [1., 0., 0., 1., 2., 1.])
assert_equal(list(h.merge_bins([(1, 2)]).y(overflow=True)),
[1., 0., 1., 2., 1.])
assert_equal(list(h.merge_bins([(-3, -2)]).y(overflow=True)),
[1., 0., 0., 3., 1.])
assert_equal(list(h.merge_bins([(-2, -1)]).y(overflow=True)),
[1., 0., 0., 1., 3., 0.])
assert_equal(list(h.merge_bins([(0, 1), (-2, -1)]).y(overflow=True)),
[0., 1., 0., 1., 3., 0.])
assert_equal(
list(h.merge_bins([(0, 1)]).merge_bins([(-2, -1)]).y(overflow=True)),
[0., 1., 0., 1., 3., 0.])
assert_equal(list(h.merge_bins([(1, 2), (3, 4)]).y(overflow=True)),
[1., 0., 3., 1.])
def test_integral_error():
h = Hist(1, 0, 1)
h.FillRandom('gaus')
ref_integral, ref_error = h.integral(error=True)
h1 = Hist(10, 0, 1)
h1.FillRandom('gaus')
integral, error = h1.integral(error=True)
assert_almost_equal(integral, ref_integral)
assert_almost_equal(error, ref_error)
h2 = Hist2D(10, 0, 1, 10, 0, 1)
h2.FillRandom(F2('x+y'))
integral, error = h2.integral(error=True)
assert_almost_equal(integral, ref_integral)
assert_almost_equal(error, ref_error)
h3 = Hist3D(10, 0, 1, 10, 0, 1, 10, 0, 1)
h3.FillRandom(F3('x+y+z'))
integral, error = h3.integral(error=True)
assert_almost_equal(integral, ref_integral)
assert_almost_equal(error, ref_error)
def function2th3(function, binsx, binsy, binsz, titlex='', titley='', titlez=''):
histo = Hist3D(*(binsx+binsy+binsz))
histo.SetXTitle(titlex)
histo.SetYTitle(titley)
histo.SetZTitle(titlez)
# Prepare array of inputs, one entry for each bin
values = []
for bx in histo.bins_range(0):
x = histo.GetXaxis().GetBinCenter(bx)
for by in histo.bins_range(1):
y = histo.GetYaxis().GetBinCenter(by)
for bz in histo.bins_range(2):
z = histo.GetZaxis().GetBinCenter(bz)
values.append([x,y,z])
# Call function for each value
results = function(np.array(values))
for result,value in zip(results, values):
bx = histo.GetXaxis().FindBin(value[0])
by = histo.GetYaxis().FindBin(value[1])
bz = histo.GetZaxis().FindBin(value[2])
histo[bx,by,bz].value = result
return histo
# creating histograms from rootpy.plotting import Hist3D # variable width bins hist3d = Hist3D([0, 3, 10, 100], [2.3, 4.2, 5.8, 10, 20, 25.5], [-100, 0, 20]) # easy to mix variable and fixed width bins with rootpy hist3d = Hist3D(3, 0, 5, [2.3, 4.2, 5.8, 10, 20, 25.5], [-100, 0, 20])
canvas.SetBottomMargin(0.15)
canvas.SetTopMargin(0.10)
canvas.SetRightMargin(0.05)
h_simple.Draw()
# create the legend
legend = Legend([h_simple],
pad=canvas,
header='Header',
leftmargin=0.05,
rightmargin=0.5)
legend.Draw()
# 2D and 3D histograms are handled in the same way
# the constructor arguments are repetitions of #bins, left bound, right bound.
h2d = Hist2D(10, 0, 1, 50, -40, 10, name='2d hist')
h3d = Hist3D(3, -1, 4, 10, -1000, -200, 2, 0, 1, name='3d hist')
# variable-width bins may be created by passing the bin edges directly:
h1d_variable = Hist([1, 4, 10, 100])
h2d_variable = Hist2D([2, 4, 7, 100, 200], [-100, -50, 0, 10, 20])
h3d_variable = Hist3D([1, 3, 10], [20, 50, 100], [-10, -5, 10, 20])
# variable-width and constant-width bins can be mixed:
h2d_mixed = Hist2D([2, 10, 30], 10, 1, 5)
# wait for you to close all open canvases before exiting
# wait() will have no effect if ROOT is in batch mode:
#ROOT.gROOT.SetBatch(True)
#wait()
def createToyTraining(self, rootFile, numberEvents):
self._f = root_open(rootFile, "read")
self._hJetPtIn = self._f.Get(
"AliJJetJtTask/AliJJetJtHistManager/JetPt/JetPtNFin{:02d}".format(
2))
self._hZIn = self._f.Get(
"AliJJetJtTask/AliJJetJtHistManager/Z/ZNFin{:02d}".format(2))
LimL = 0.1
LimH = 500
logBW = (TMath.Log(LimH) - TMath.Log(LimL)) / self._NBINS
self._LogBinsX = [
LimL * math.exp(ij * logBW) for ij in range(0, self._NBINS + 1)
]
self._hJetPtMeas = Hist(self._LogBinsX)
self._hJetPtTrue = Hist(self._LogBinsX)
self._myRandom = TRandom3(self._randomSeed)
if self._fEff is None:
self._fEff = TF1("fEff", "1-0.5*exp(-x)")
if self._jetBinBorders is None:
self._jetBinBorders = [5, 10, 20, 30, 40, 60, 80, 100, 150, 500]
self._hJetPtMeasCoarse = Hist(self._jetBinBorders)
self._hJetPtTrueCoarse = Hist(self._jetBinBorders)
low = 0.01
high = 10
BinW = (TMath.Log(high) - TMath.Log(low)) / self._NBINSJt
self._LogBinsJt = [
low * math.exp(i * BinW) for i in range(self._NBINSJt + 1)
]
self._jetBinBorders = self._jetBinBorders
self._jetPtBins = [
(a, b)
for a, b in zip(self._jetBinBorders, self._jetBinBorders[1:])
]
self._hJtTrue2D = Hist2D(self._LogBinsJt, self._jetBinBorders)
self._hJtMeas2D = Hist2D(self._LogBinsJt, self._jetBinBorders)
self._hJtFake2D = Hist2D(self._LogBinsJt, self._jetBinBorders)
# Histogram to store jT with respect to the leading hadron
self._hJtTestMeas2D = Hist2D(
self._LogBinsJt, self._jetBinBorders) # Needs a better name
self._hJtTestTrue2D = Hist2D(
self._LogBinsJt, self._jetBinBorders) # Needs a better name
self._responseJetPt = RooUnfoldResponse(self._hJetPtMeas,
self._hJetPtTrue)
self._responseJetPtCoarse = RooUnfoldResponse(self._hJetPtMeasCoarse,
self._hJetPtTrueCoarse)
self._hresponseJetPt = Hist2D(self._jetBinBorders, self._jetBinBorders)
self._hresponseJetPtCoarse = Hist2D(self._jetBinBorders,
self._jetBinBorders)
# Histogram index is jet pT index, Bin 0 is 5-10 GeV
# Histogram X axis is observed jT, Bin 0 is underflow
# Histogram Y axis is observed jet Pt, Bin 0 is underflow
# Histogram Z axis is True jT, Bin 0 is underflow
self._2Dresponses = [
Hist3D(self._LogBinsJt, self._jetBinBorders, self._LogBinsJt)
for i in self._jetPtBins
]
self._misses2D = Hist2D(self._LogBinsJt, self._jetBinBorders)
self._fakes2D = Hist2D(self._LogBinsJt, self._jetBinBorders)
self._numberJetsMeasBin = [0 for i in self._jetBinBorders]
self._numberJetsTrueBin = [0 for i in self._jetBinBorders]
self._numberJetsTestMeasBin = [0 for i in self._jetBinBorders]
self._numberJetsTestTrueBin = [0 for i in self._jetBinBorders]
self._numberFakesBin = [0 for i in self._jetBinBorders]
ieout = numberEvents / 10
if ieout > 20000:
ieout = 20000
start_time = datetime.now()
print("Processing MC Training Events")
for ievt in range(numberEvents):
tracksTrue = []
tracksMeas = [0 for x in range(100)]
if ievt % ieout == 0 and ievt > 0:
time_elapsed = datetime.now() - start_time
time_left = timedelta(seconds=time_elapsed.total_seconds() *
1.0 * (numberEvents - ievt) / ievt)
print("Event {} [{:.2f}%] Time Elapsed: {} ETA: {}".format(
ievt,
100.0 * ievt / numberEvents,
fmtDelta(time_elapsed),
fmtDelta(time_left),
))
jetTrue = TVector3(0, 0, 0)
jetMeas = TVector3(0, 0, 0)
jetPt = self._hJetPtIn.GetRandom()
remainder = jetPt
if jetPt < 5:
continue
nt = 0
nt_meas = 0
while remainder > 0:
trackPt = self._hZIn.GetRandom() * jetPt
if trackPt < remainder:
track = TVector3()
remainder = remainder - trackPt
else:
trackPt = remainder
remainder = -1
if trackPt > 0.15:
track.SetPtEtaPhi(
trackPt,
self._myRandom.Gaus(0, 0.1),
self._myRandom.Gaus(math.pi, 0.2),
)
tracksTrue.append(track)
jetTrue += track
if self._fEff.Eval(trackPt) > self._myRandom.Uniform(0, 1):
tracksMeas[nt] = 1
jetMeas += track
nt_meas += 1
else:
tracksMeas[nt] = 0
nt += 1
fakes = []
for it in range(self._fakeRate * 100):
if self._myRandom.Uniform(0, 1) > 0.99:
fake = TVector3()
fake.SetPtEtaPhi(
self._myRandom.Uniform(0.15, 1),
self._myRandom.Gaus(0, 0.1),
self._myRandom.Gaus(math.pi, 0.2),
)
fakes.append(fake)
jetMeas += fake
self._responseJetPt.Fill(jetMeas.Pt(), jetTrue.Pt())
self._responseJetPtCoarse.Fill(jetMeas.Pt(), jetTrue.Pt())
self._hresponseJetPt.Fill(jetMeas.Pt(), jetTrue.Pt())
self._hresponseJetPtCoarse.Fill(jetMeas.Pt(), jetTrue.Pt())
ij_meas = GetBin(self._jetBinBorders, jetMeas.Pt())
ij_true = GetBin(self._jetBinBorders, jetTrue.Pt())
if nt < 5 or nt_meas < 5:
continue
if ij_meas >= 0:
self._numberJetsMeasBin[ij_meas] += 1
if ij_true >= 0:
self._numberJetsTrueBin[ij_true] += 1
for track, it in zip(tracksTrue, range(100)):
zTrue = (track * jetTrue.Unit()) / jetTrue.Mag()
jtTrue = (track - scaleJet(jetTrue, zTrue)).Mag()
if ij_meas >= 0:
if tracksMeas[it] == 1:
zMeas = (track * jetMeas.Unit()) / jetMeas.Mag()
jtMeas = (track - scaleJet(jetMeas, zMeas)).Mag()
self._2Dresponses[ij_true].Fill(
jtMeas, jetMeas.Pt(), jtTrue)
else:
self._misses2D.Fill(jtTrue, jetTrue.Pt())
if ij_meas >= 0:
for fake in fakes:
zFake = (fake * jetMeas.Unit()) / jetMeas.Mag()
jtFake = (fake - scaleJet(jetMeas, zFake)).Mag()
if self._weight:
self._hJtFake2D.Fill(jtFake, jetMeas.Pt(),
1.0 / jtFake)
else:
self._hJtFake2D.Fill(jtFake, jetMeas.Pt())
if self._fillFakes:
self._fakes2D.Fill(jtFake, jetMeas.Pt())
print("Event {} [{:.2f}%] Time Elapsed: {}".format(
numberEvents, 100.0, fmtDelta(time_elapsed)))
self._numberJetsMeasTrain = sum(self._numberJetsMeasBin)
===================================
Fill a histogram with a NumPy array
===================================
This example demonstrates how a 1D, 2D, or 3D ROOT histogram can be efficiently
filled with a NumPy array.
"""
print __doc__
import rootpy
rootpy.log.basic_config_colorized()
from rootpy.interactive import wait
from rootpy.plotting import Canvas, Hist, Hist2D, Hist3D
import numpy as np
c1 = Canvas()
a = Hist(1000, -5, 5)
a.fill_array(np.random.randn(1000000))
a.Draw('hist')
c2 = Canvas()
b = Hist2D(100, -5, 5, 100, -5, 5)
b.fill_array(np.random.randn(1000000, 2))
b.Draw('LEGO2Z0')
c3 = Canvas()
c = Hist3D(10, -5, 5, 10, -5, 5, 10, -5, 5)
c.markersize = .3
c.fill_array(np.random.randn(10000, 3))
c.Draw('SCAT')
wait(True)
def main():
if len(sys.argv) < 3:
print("Usage: ToyMC [numberEvents] [randomSeed]")
return
numberEvents = int(sys.argv[1])
seed = int(sys.argv[2])
print(
"==================================== TRAIN ===================================="
)
f = root_open(
"legotrain_350_20161117-2106_LHCb4_fix_CF_pPb_MC_ptHardMerged.root", "read"
)
hJetPt = f.Get("AliJJetJtTask/AliJJetJtHistManager/JetPt/JetPtNFin{:02d}".format(2))
hZ = f.Get("AliJJetJtTask/AliJJetJtHistManager/Z/ZNFin{:02d}".format(2))
FillFakes = False
dummy_variable = True
weight = True
NBINS = 50
LimL = 0.1
LimH = 500
logBW = (TMath.Log(LimH) - TMath.Log(LimL)) / NBINS
LogBinsX = [LimL * math.exp(ij * logBW) for ij in range(0, NBINS + 1)]
hJetPtMeas = Hist(LogBinsX)
hJetPtTrue = Hist(LogBinsX)
myRandom = TRandom3(seed)
fEff = TF1("fEff", "1-0.5*exp(-x)")
jetBinBorders = [5, 10, 20, 30, 40, 60, 80, 100, 150, 500]
hJetPtMeasCoarse = Hist(jetBinBorders)
hJetPtTrueCoarse = Hist(jetBinBorders)
NBINSJt = 64
low = 0.01
high = 10
BinW = (TMath.Log(high) - TMath.Log(low)) / NBINSJt
LogBinsJt = [low * math.exp(i * BinW) for i in range(NBINSJt + 1)]
hJtTrue = Hist(LogBinsJt)
hJtMeas = Hist(LogBinsJt)
hJtFake = Hist(LogBinsJt)
LogBinsPt = jetBinBorders
jetPtBins = [(a, b) for a, b in zip(jetBinBorders, jetBinBorders[1:])]
hJtTrue2D = Hist2D(LogBinsJt, LogBinsPt)
hJtMeas2D = Hist2D(LogBinsJt, LogBinsPt)
hJtFake2D = Hist2D(LogBinsJt, LogBinsPt)
hJtMeasBin = [Hist(LogBinsJt) for i in jetBinBorders]
hJtTrueBin = [Hist(LogBinsJt) for i in jetBinBorders]
response = RooUnfoldResponse(hJtMeas, hJtTrue)
response2D = RooUnfoldResponse(hJtMeas2D, hJtTrue2D)
responseBin = [RooUnfoldResponse(hJtMeas, hJtTrue) for i in jetBinBorders]
responseJetPt = RooUnfoldResponse(hJetPtMeas, hJetPtTrue)
responseJetPtCoarse = RooUnfoldResponse(hJetPtMeasCoarse, hJetPtTrueCoarse)
# Histogram index is jet pT index, Bin 0 is 5-10 GeV
# Histogram X axis is observed jT, Bin 0 is underflow
# Histogram Y axis is observed jet Pt, Bin 0 is underflow
# Histogram Z axis is True jT, Bin 0 is underflow
responses = [Hist3D(LogBinsJt, LogBinsPt, LogBinsJt) for i in jetPtBins]
misses = Hist2D(LogBinsJt, LogBinsPt)
fakes2D = Hist2D(LogBinsJt, LogBinsPt)
outFile = TFile("tuple.root", "recreate")
responseTuple = TNtuple(
"responseTuple", "responseTuple", "jtObs:ptObs:jtTrue:ptTrue"
)
hMultiTrue = Hist(50, 0, 50)
hMultiMeas = Hist(50, 0, 50)
hZMeas = Hist(50, 0, 1)
hZTrue = Hist(50, 0, 1)
hZFake = Hist(50, 0, 1)
responseMatrix = Hist2D(LogBinsJt, LogBinsJt)
numberJets = 0
numberFakes = 0
numberJetsMeasBin = [0 for i in jetBinBorders]
numberJetsTrueBin = [0 for i in jetBinBorders]
numberFakesBin = [0 for i in jetBinBorders]
ieout = numberEvents / 10
if ieout > 10000:
ieout = 10000
fakeRate = 1
start_time = datetime.now()
print("Processing Training Events")
for ievt in range(numberEvents):
tracksTrue = []
tracksMeas = [0 for x in range(100)]
if ievt % ieout == 0 and ievt > 0:
time_elapsed = datetime.now() - start_time
time_left = timedelta(
seconds=time_elapsed.total_seconds()
* 1.0
* (numberEvents - ievt)
/ ievt
)
print(
"Event {} [{:.2f}%] Time Elapsed: {} ETA: {}".format(
ievt,
100.0 * ievt / numberEvents,
fmtDelta(time_elapsed),
fmtDelta(time_left),
)
)
jetTrue = TVector3(0, 0, 0)
jetMeas = TVector3(0, 0, 0)
jetPt = hJetPt.GetRandom()
remainder = jetPt
if jetPt < 5:
continue
nt = 0
nt_meas = 0
while remainder > 0:
trackPt = hZ.GetRandom() * jetPt
if trackPt < remainder:
track = TVector3()
remainder = remainder - trackPt
else:
trackPt = remainder
remainder = -1
if trackPt > 0.15:
track.SetPtEtaPhi(
trackPt, myRandom.Gaus(0, 0.1), myRandom.Gaus(math.pi, 0.2)
)
tracksTrue.append(track)
jetTrue += track
if fEff.Eval(trackPt) > myRandom.Uniform(0, 1):
tracksMeas[nt] = 1
jetMeas += track
nt_meas += 1
else:
tracksMeas[nt] = 0
nt += 1
fakes = []
for it in range(fakeRate * 100):
if myRandom.Uniform(0, 1) > 0.99:
fake = TVector3()
fake.SetPtEtaPhi(
myRandom.Uniform(0.15, 1),
myRandom.Gaus(0, 0.1),
myRandom.Gaus(math.pi, 0.2),
)
fakes.append(fake)
jetMeas += fake
hJetPtMeas.Fill(jetMeas.Pt())
hJetPtTrue.Fill(jetTrue.Pt())
responseJetPt.Fill(jetMeas.Pt(), jetTrue.Pt())
responseJetPtCoarse.Fill(jetMeas.Pt(), jetTrue.Pt())
hMultiTrue.Fill(nt)
hMultiMeas.Fill(nt_meas)
ij_meas = GetBin(jetBinBorders, jetMeas.Pt())
ij_true = GetBin(jetBinBorders, jetTrue.Pt())
if nt < 5 or nt_meas < 5:
continue
numberJets += 1
if ij_meas >= 0:
numberJetsMeasBin[ij_meas] += 1
hJetPtMeasCoarse.Fill(jetMeas.Pt())
if ij_true >= 0:
numberJetsTrueBin[ij_true] += 1
hJetPtTrueCoarse.Fill(jetTrue.Pt())
for track, it in zip(tracksTrue, range(100)):
zTrue = (track * jetTrue.Unit()) / jetTrue.Mag()
jtTrue = (track - scaleJet(jetTrue, zTrue)).Mag()
hZTrue.Fill(zTrue)
if ij_true >= 0:
if weight:
hJtTrue.Fill(jtTrue, 1.0 / jtTrue)
hJtTrueBin[ij_true].Fill(jtTrue, 1.0 / jtTrue)
hJtTrue2D.Fill(jtTrue, jetTrue.Pt(), 1.0 / jtTrue)
else:
hJtTrue.Fill(jtTrue)
hJtTrueBin[ij_true].Fill(jtTrue)
hJtTrue2D.Fill(jtTrue, jetTrue.Pt())
if ij_meas >= 0:
if tracksMeas[it] == 1:
zMeas = (track * jetMeas.Unit()) / jetMeas.Mag()
jtMeas = (track - scaleJet(jetMeas, zMeas)).Mag()
hZMeas.Fill(zMeas)
if weight:
hJtMeasBin[ij_meas].Fill(jtMeas, 1.0 / jtMeas)
hJtMeas.Fill(jtMeas, 1.0 / jtMeas)
hJtMeas2D.Fill(jtMeas, jetMeas.Pt(), 1.0 / jtMeas)
else:
hJtMeas.Fill(jtMeas)
hJtMeasBin[ij_meas].Fill(jtMeas)
hJtMeas2D.Fill(jtMeas, jetMeas.Pt())
response.Fill(jtMeas, jtTrue)
responseBin[ij_true].Fill(jtMeas, jtTrue)
response2D.Fill(jtMeas, jetMeas.Pt(), jtTrue, jetTrue.Pt())
responseMatrix.Fill(jtMeas, jtTrue)
responses[ij_true].Fill(jtMeas, jetMeas.Pt(), jtTrue)
responseTuple.Fill(jtMeas, jetMeas.Pt(), jtTrue, jetTrue.Pt())
else:
response.Miss(jtTrue)
responseBin[ij_true].Miss(jtTrue)
response2D.Miss(jtTrue, jetTrue.Pt())
misses.Fill(jtTrue, jetTrue.Pt())
responseTuple.Fill(-1, -1, jtTrue, jetTrue.Pt())
if ij_meas >= 0:
for fake in fakes:
zFake = (fake * jetMeas.Unit()) / jetMeas.Mag()
jtFake = (fake - scaleJet(jetMeas, zFake)).Mag()
hZMeas.Fill(zFake)
hZFake.Fill(zFake)
if weight:
hJtMeas.Fill(jtFake, 1.0 / jtFake)
hJtMeasBin[ij_meas].Fill(jtFake, 1.0 / jtFake)
hJtMeas2D.Fill(jtFake, jetMeas.Pt(), 1.0 / jtFake)
hJtFake2D.Fill(jtFake, jetMeas.Pt(), 1.0 / jtFake)
hJtFake.Fill(jtFake, 1.0 / jtFake)
else:
hJtMeas.Fill(jtFake)
hJtMeasBin[ij_meas].Fill(jtFake)
hJtMeas2D.Fill(jtFake, jetMeas.Pt())
hJtFake2D.Fill(jtFake, jetMeas.Pt())
hJtFake.Fill(jtFake)
if FillFakes:
response.Fake(jtFake)
responseBin[ij_true].Fake(jtFake)
response2D.Fake(jtFake, jetMeas.Pt())
fakes2D.Fill(jtFake, jetMeas.Pt())
responseTuple.Fill(jtFake, jetMeas.Pt(), -1, -1)
numberFakes += 1
numberFakesBin[ij_true] += 1
response2Dtest = make2Dresponse(
responses, jetPtBins, hJtMeas2D, hJtTrue2D, misses=misses, fakes=fakes2D
)
if dummy_variable:
hJetPtMeas.Reset()
hJetPtTrue.Reset()
hMultiTrue.Reset()
hMultiMeas.Reset()
hJetPtMeasCoarse.Reset()
hJetPtTrueCoarse.Reset()
hZTrue.Reset()
hZMeas.Reset()
hJtTrue.Reset()
hJtTrue2D.Reset()
hJtMeas.Reset()
hJtMeas2D.Reset()
hJtFake.Reset()
hJtFake2D.Reset()
for h, h2 in zip(hJtTrueBin, hJtMeasBin):
h.Reset()
h2.Reset()
numberJetsMeasBin = [0 for i in jetBinBorders]
numberJetsTrueBin = [0 for i in jetBinBorders]
numberJets = 0
print("Create testing data")
start_time = datetime.now()
numberEvents = numberEvents / 2
for ievt in range(numberEvents):
tracksTrue = []
tracksMeas = [0 for x in range(100)]
if ievt % ieout == 0 and ievt > 0:
time_elapsed = datetime.now() - start_time
time_left = timedelta(
seconds=time_elapsed.total_seconds()
* 1.0
* (numberEvents - ievt)
/ ievt
)
print(
"Event {} [{:.2f}%] Time Elapsed: {} ETA: {}".format(
ievt,
100.0 * ievt / numberEvents,
fmtDelta(time_elapsed),
fmtDelta(time_left),
)
)
jetTrue = TVector3(0, 0, 0)
jetMeas = TVector3(0, 0, 0)
jetPt = hJetPt.GetRandom()
remainder = jetPt
if jetPt < 5:
continue
nt = 0
nt_meas = 0
while remainder > 0:
trackPt = hZ.GetRandom() * jetPt
if trackPt < remainder:
track = TVector3()
remainder = remainder - trackPt
else:
trackPt = remainder
remainder = -1
if trackPt > 0.15:
track.SetPtEtaPhi(
trackPt, myRandom.Gaus(0, 0.1), myRandom.Gaus(math.pi, 0.2)
)
tracksTrue.append(track)
jetTrue += track
if fEff.Eval(trackPt) > myRandom.Uniform(0, 1):
tracksMeas[nt] = 1
jetMeas += track
nt_meas += 1
else:
tracksMeas[nt] = 0
nt += 1
fakes = []
for it in range(fakeRate * 100):
if myRandom.Uniform(0, 1) > 0.99:
fake = TVector3()
fake.SetPtEtaPhi(
myRandom.Uniform(0.15, 1),
myRandom.Gaus(0, 0.1),
myRandom.Gaus(math.pi, 0.2),
)
fakes.append(fake)
jetMeas += fake
hJetPtMeas.Fill(jetMeas.Pt())
hJetPtTrue.Fill(jetTrue.Pt())
hMultiTrue.Fill(nt)
hMultiMeas.Fill(nt_meas)
ij_meas = GetBin(jetBinBorders, jetMeas.Pt())
ij_true = GetBin(jetBinBorders, jetTrue.Pt())
if nt < 5 or nt_meas < 5:
continue
numberJets += 1
if ij_meas >= 0:
numberJetsMeasBin[ij_meas] += 1
hJetPtMeasCoarse.Fill(jetMeas.Pt())
if ij_true >= 0:
numberJetsTrueBin[ij_true] += 1
hJetPtTrueCoarse.Fill(jetTrue.Pt())
for track, it in zip(tracksTrue, range(100)):
zTrue = (track * jetTrue.Unit()) / jetTrue.Mag()
jtTrue = (track - scaleJet(jetTrue, zTrue)).Mag()
hZTrue.Fill(zTrue)
if ij_true >= 0:
if weight:
hJtTrue.Fill(jtTrue, 1.0 / jtTrue)
hJtTrueBin[ij_true].Fill(jtTrue, 1.0 / jtTrue)
hJtTrue2D.Fill(jtTrue, jetTrue.Pt(), 1.0 / jtTrue)
else:
hJtTrue.Fill(jtTrue)
hJtTrueBin[ij_true].Fill(jtTrue)
hJtTrue2D.Fill(jtTrue, jetTrue.Pt())
if ij_meas >= 0:
if tracksMeas[it] == 1:
zMeas = (track * jetMeas.Unit()) / jetMeas.Mag()
jtMeas = (track - scaleJet(jetMeas, zMeas)).Mag()
hZMeas.Fill(zMeas)
if weight:
hJtMeasBin[ij_meas].Fill(jtMeas, 1.0 / jtMeas)
hJtMeas.Fill(jtMeas, 1.0 / jtMeas)
hJtMeas2D.Fill(jtMeas, jetMeas.Pt(), 1.0 / jtMeas)
else:
hJtMeas.Fill(jtMeas)
hJtMeasBin[ij_meas].Fill(jtMeas)
hJtMeas2D.Fill(jtMeas, jetMeas.Pt())
if ij_meas >= 0:
for fake in fakes:
zFake = (fake * jetMeas.Unit()) / jetMeas.Mag()
jtFake = (fake - scaleJet(jetMeas, zFake)).Mag()
hZMeas.Fill(zFake)
hZFake.Fill(zFake)
if weight:
hJtMeas.Fill(jtFake, 1.0 / jtFake)
hJtMeasBin[ij_meas].Fill(jtFake, 1.0 / jtFake)
hJtMeas2D.Fill(jtFake, jetMeas.Pt(), 1.0 / jtFake)
hJtFake2D.Fill(jtFake, jetMeas.Pt(), 1.0 / jtFake)
hJtFake.Fill(jtFake, 1.0 / jtFake)
else:
hJtMeas.Fill(jtFake)
hJtMeasBin[ij_meas].Fill(jtFake)
hJtMeas2D.Fill(jtFake, jetMeas.Pt())
hJtFake2D.Fill(jtFake, jetMeas.Pt())
hJtFake.Fill(jtFake)
time_elapsed = datetime.now() - start_time
print(
"Event {} [{:.2f}%] Time Elapsed: {}".format(
numberEvents, 100.0, fmtDelta(time_elapsed)
)
)
if not FillFakes:
hJtMeas.Add(hJtFake, -1)
hJtMeas2D.Add(hJtFake2D, -1)
responseTuple.Print()
outFile.Write()
# printTuple(responseTuple)
hJtMeasProjBin = [
makeHist(hJtMeas2D.ProjectionX("histMeas{}".format(i), i, i), bins=LogBinsJt)
for i in range(1, len(jetBinBorders))
]
hJtMeasProj = makeHist(hJtMeas2D.ProjectionX("histMeas"), bins=LogBinsJt)
hJtTrueProjBin = [
makeHist(hJtTrue2D.ProjectionX("histTrue{}".format(i), i, i), bins=LogBinsJt)
for i in range(1, len(jetBinBorders))
]
hJtTrueProj = makeHist(hJtTrue2D.ProjectionX("histTrue"), bins=LogBinsJt)
hJtFakeProjBin = [
makeHist(hJtFake2D.ProjectionX("histFake{}".format(i), i, i), bins=LogBinsJt)
for i in range(1, len(jetBinBorders))
]
if not FillFakes:
for h, h2 in zip(hJtMeasBin, hJtFakeProjBin):
h.Add(h2, -1)
for h in (
hJtMeasProj,
hJtTrueProj,
hJtMeas,
hJtTrue,
hJtFake,
hZFake,
hZMeas,
hZTrue,
):
h.Scale(1.0 / numberJets, "width")
for meas, true, n_meas, n_true in zip(
hJtMeasBin, hJtTrueBin, numberJetsMeasBin, numberJetsTrueBin
):
if n_meas > 0:
meas.Scale(1.0 / n_meas, "width")
if n_true > 0:
true.Scale(1.0 / n_true, "width")
numberJetsMeasFromHist = [
hJetPtMeasCoarse.GetBinContent(i)
for i in range(1, hJetPtMeasCoarse.GetNbinsX() + 1)
]
numberJetsTrueFromHist = [
hJetPtTrueCoarse.GetBinContent(i)
for i in range(1, hJetPtTrueCoarse.GetNbinsX() + 1)
]
print("Total number of jets: {}".format(numberJets))
print("Total number of fakes: {}".format(numberFakes))
print("Measured jets by bin")
print(numberJetsMeasBin)
print(numberJetsMeasFromHist)
print("True jets by bin")
print(numberJetsTrueBin)
print(numberJetsTrueFromHist)
hRecoJetPtCoarse = unfoldJetPt(hJetPtMeasCoarse, responseJetPtCoarse, jetBinBorders)
numberJetsFromReco = [
hRecoJetPtCoarse.GetBinContent(i)
for i in range(1, hRecoJetPtCoarse.GetNbinsX())
]
print("Unfolded jet numbers by bin:")
print(numberJetsFromReco)
print("Fakes by bin")
print(numberFakesBin)
print(
"==================================== UNFOLD ==================================="
)
unfold = RooUnfoldBayes(response, hJtMeas, 4) # OR
unfoldSVD = RooUnfoldSvd(response, hJtMeas, 20) # OR
unfold2D = RooUnfoldBayes(response2D, hJtMeas2D, 4)
for u in (unfold, unfoldSVD, unfold2D):
u.SetVerbose(0)
# response2Dtest = makeResponseFromTuple(responseTuple,hJtMeas2D,hJtTrue2D)
unfold2Dtest = RooUnfoldBayes(response2Dtest, hJtMeas2D, 4)
unfoldBin = [
RooUnfoldBayes(responseBin[i], hJtMeasBin[i]) for i in range(len(jetBinBorders))
]
for u in unfoldBin:
u.SetVerbose(0)
hRecoBayes = makeHist(unfold.Hreco(), bins=LogBinsJt)
hRecoSVD = makeHist(unfoldSVD.Hreco(), bins=LogBinsJt)
hRecoBin = [
makeHist(unfoldBin[i].Hreco(), bins=LogBinsJt)
for i in range(len(jetBinBorders))
]
hReco2D = make2DHist(unfold2D.Hreco(), xbins=LogBinsJt, ybins=LogBinsPt)
hReco2Dtest = make2DHist(unfold2Dtest.Hreco(), xbins=LogBinsJt, ybins=LogBinsPt)
hRecoJetPt = unfoldJetPt(hJetPtMeas, responseJetPt, LogBinsX)
hReco2DProjBin = [
makeHist(hReco2D.ProjectionX("histReco{}".format(i), i, i), bins=LogBinsJt)
for i in range(1, len(jetBinBorders))
]
hReco2DTestProjBin = [
makeHist(
hReco2Dtest.ProjectionX("histRecoTest{}".format(i), i, i), bins=LogBinsJt
)
for i in range(1, len(jetBinBorders))
]
hReco2DProj = makeHist(hReco2D.ProjectionX("histReco"), bins=LogBinsJt)
hReco2DProj.Scale(1.0 / numberJets, "width")
for h, h2, n in zip(hReco2DProjBin, hReco2DTestProjBin, numberJetsFromReco):
if n > 0:
h.Scale(1.0 / n, "width")
h2.Scale(1.0 / n, "width")
# unfold.PrintTable (cout, hJtTrue)
for h, h2, nj in zip(hJtMeasProjBin, hJtFakeProjBin, numberJetsMeasBin):
if nj > 0:
h.Scale(1.0 / nj, "width")
h2.Scale(1.0 / nj, "width")
# else:
# print("nj is 0 for {}".format(h.GetName()))
for h, nj in zip(hJtTrueProjBin, numberJetsTrueBin):
if nj > 0:
h.Scale(1.0 / nj, "width")
# draw8grid(hJtMeasBin[1:],hJtTrueBin[1:],jetPtBins[1:],xlog = True,ylog = True,name="newfile.pdf",proj = hJtMeasProjBin[2:], unf2d = hReco2DProjBin[2:], unf=hRecoBin[1:])
if numberEvents > 1000:
if numberEvents > 1000000:
filename = "ToyMC_{}M_events.pdf".format(numberEvents / 1000000)
else:
filename = "ToyMC_{}k_events.pdf".format(numberEvents / 1000)
else:
filename = "ToyMC_{}_events.pdf".format(numberEvents)
draw8gridcomparison(
hJtMeasBin,
hJtTrueBin,
jetPtBins,
xlog=True,
ylog=True,
name=filename,
proj=None,
unf2d=hReco2DProjBin,
unf2dtest=hReco2DTestProjBin,
unf=hRecoBin,
fake=hJtFakeProjBin,
start=1,
stride=1,
)
drawQA(
hJtMeas,
hJtTrue,
hJtFake,
hRecoBayes,
hRecoSVD,
hReco2DProj,
hZ,
hZTrue,
hZMeas,
hZFake,
hMultiMeas,
hMultiTrue,
hJetPt,
hJetPtTrue,
hJetPtMeas,
hRecoJetPt,
responseMatrix,
)
outFile.Close()