def test_vectors( self ):
h_signal = adjust_overflow_to_limit( self.h_signal, x_min, x_max )
h_signal.Scale(1/h_signal.Integral())
h_bkg1 = adjust_overflow_to_limit( self.h_bkg1, x_min, x_max )
h_bkg1.Scale(1/h_bkg1.Integral())
signal = list( h_signal.y() )
bkg1 = list( h_bkg1.y() )
v_from_fit_data = self.fit_data_1.vectors
v_from_single_collection = self.single_collection.vectors()
# v_from_collection = self.collection_1.vectors( 'signal region' )
# v_from_collection_1 = self.collection_1.vectors()['signal region']
self.assertEqual(signal, v_from_fit_data['signal'])
self.assertEqual(bkg1, v_from_fit_data['bkg1'])
self.assertEqual(signal, v_from_single_collection['signal'])
self.assertEqual(bkg1, v_from_single_collection['bkg1'])
def test_vectors(self):
h_signal = adjust_overflow_to_limit(self.h_signal, x_min, x_max)
h_signal.Scale(1 / h_signal.Integral())
h_bkg1 = adjust_overflow_to_limit(self.h_bkg1, x_min, x_max)
h_bkg1.Scale(1 / h_bkg1.Integral())
signal = list(h_signal.y())
bkg1 = list(h_bkg1.y())
v_from_fit_data = self.fit_data_1.vectors
v_from_single_collection = self.single_collection.vectors()
# v_from_collection = self.collection_1.vectors( 'signal region' )
# v_from_collection_1 = self.collection_1.vectors()['signal region']
self.assertEqual(signal, v_from_fit_data['signal'])
self.assertEqual(bkg1, v_from_fit_data['bkg1'])
self.assertEqual(signal, v_from_single_collection['signal'])
self.assertEqual(bkg1, v_from_single_collection['bkg1'])
def test_adjust_overflow_to_limit( self ):
x_min = 40
x_max = 80
adjusted = adjust_overflow_to_limit(self.h1, x_min, x_max)
# number of events should be unchanged
# the adjusted histogram should have no overflow for this example
self.assertEqual(self.h1.integral( overflow = True ), adjusted.Integral())
# first bin (x_min) should contain all events
# with x <= x_min
x_min_bin = self.h1.FindBin(x_min)
x_max_bin = self.h1.FindBin(x_max)
self.assertEqual(self.h1.integral(0, x_min_bin),
adjusted.GetBinContent(x_min_bin))
# last bin (x_max) should contain all events
# with x >= x_max
self.assertEqual( self.h1.integral( x_max_bin, self.h1.nbins() + 1 ),
adjusted.GetBinContent( x_max_bin ) )
def test_adjust_overflow_to_limit(self):
x_min = 40
x_max = 80
adjusted = adjust_overflow_to_limit(self.h1, x_min, x_max)
# number of events should be unchanged
# the adjusted histogram should have no overflow for this example
self.assertEqual(self.h1.integral(overflow=True), adjusted.Integral())
# first bin (x_min) should contain all events
# with x <= x_min
x_min_bin = self.h1.FindBin(x_min)
x_max_bin = self.h1.FindBin(x_max)
self.assertEqual(self.h1.integral(0, x_min_bin),
adjusted.GetBinContent(x_min_bin))
# last bin (x_max) should contain all events
# with x >= x_max
self.assertEqual(self.h1.integral(x_max_bin,
self.h1.nbins() + 1),
adjusted.GetBinContent(x_max_bin))
def test_adjust_overflow_to_limit_simple( self ):
x_min = 0
x_max = 95
adjusted = adjust_overflow_to_limit(self.simple, x_min, x_max)
# for entry_1, entry_2 in zip(hist_to_value_error_tuplelist(self.simple), hist_to_value_error_tuplelist(adjusted)):
# print entry_1, entry_2
# print self.simple.integral( overflow = True ), adjusted.integral()
# print self.simple.GetBinContent(1), self.simple.GetBinContent(self.simple.nbins())
# number of events should be unchanged
# the adjusted histogram should have no overflow for this example
self.assertEqual( self.simple.integral( overflow = True ), adjusted.integral() )
# first bin (x_min) should contain all events
# with x <= x_min
x_min_bin = self.simple.FindBin(x_min)
x_max_bin = self.simple.FindBin(x_max)
self.assertEqual(self.simple.integral(0, x_min_bin),
adjusted.GetBinContent(x_min_bin))
# last bin (x_max) should contain all events
# with x >= x_max
self.assertEqual( self.simple.integral( x_max_bin, self.simple.nbins() + 1),
adjusted.GetBinContent( x_max_bin ) )
def test_normalisation(self):
normalisation = {
name: adjust_overflow_to_limit(histogram, x_min, x_max).Integral()
for name, histogram in self.histograms_1.iteritems()
}
normalisation_from_fit_data = self.fit_data_1.normalisation
normalisation_from_single_collection = self.single_collection.mc_normalisation(
)
normalisation_from_collection = self.collection_1.mc_normalisation(
'signal region')
normalisation_from_collection_1 = self.collection_1.mc_normalisation(
)['signal region']
for sample in normalisation.keys():
self.assertEqual(normalisation[sample],
normalisation_from_fit_data[sample])
self.assertEqual(normalisation[sample],
normalisation_from_single_collection[sample])
self.assertEqual(normalisation[sample],
normalisation_from_collection[sample])
self.assertEqual(normalisation[sample],
normalisation_from_collection_1[sample])
# data normalisation
normalisation = self.h_data.integral(overflow=True)
normalisation_from_fit_data = self.fit_data_1.n_data()
normalisation_from_single_collection = self.single_collection.n_data()
normalisation_from_collection = self.collection_1.n_data(
'signal region')
normalisation_from_collection_1 = self.collection_1.n_data(
)['signal region']
self.assertEqual(normalisation, normalisation_from_fit_data)
self.assertEqual(normalisation, normalisation_from_single_collection)
self.assertEqual(normalisation, normalisation_from_collection)
self.assertEqual(normalisation, normalisation_from_collection_1)
self.assertAlmostEqual(normalisation,
self.collection_1.max_n_data(),
delta=1)
def test_normalisation( self ):
normalisation = {name:adjust_overflow_to_limit(histogram, x_min, x_max).Integral() for name, histogram in self.histograms_1.iteritems()}
normalisation_from_fit_data = self.fit_data_1.normalisation
normalisation_from_single_collection = self.single_collection.mc_normalisation()
normalisation_from_collection = self.collection_1.mc_normalisation( 'signal region' )
normalisation_from_collection_1 = self.collection_1.mc_normalisation()['signal region']
for sample in normalisation.keys():
self.assertEqual( normalisation[sample], normalisation_from_fit_data[sample] )
self.assertEqual( normalisation[sample], normalisation_from_single_collection[sample] )
self.assertEqual( normalisation[sample], normalisation_from_collection[sample] )
self.assertEqual( normalisation[sample], normalisation_from_collection_1[sample] )
# data normalisation
normalisation = self.h_data.integral( overflow = True )
normalisation_from_fit_data = self.fit_data_1.n_data()
normalisation_from_single_collection = self.single_collection.n_data()
normalisation_from_collection = self.collection_1.n_data( 'signal region' )
normalisation_from_collection_1 = self.collection_1.n_data()['signal region']
self.assertEqual( normalisation, normalisation_from_fit_data )
self.assertEqual( normalisation, normalisation_from_single_collection )
self.assertEqual( normalisation, normalisation_from_collection )
self.assertEqual( normalisation, normalisation_from_collection_1 )
self.assertAlmostEqual(normalisation, self.collection_1.max_n_data(), delta = 1 )
def test_adjust_overflow_to_limit_simple(self):
x_min = 0
x_max = 95
adjusted = adjust_overflow_to_limit(self.simple, x_min, x_max)
# for entry_1, entry_2 in zip(hist_to_value_error_tuplelist(self.simple), hist_to_value_error_tuplelist(adjusted)):
# print entry_1, entry_2
# print self.simple.integral( overflow = True ), adjusted.integral()
# print self.simple.GetBinContent(1), self.simple.GetBinContent(self.simple.nbins())
# number of events should be unchanged
# the adjusted histogram should have no overflow for this example
self.assertEqual(self.simple.integral(overflow=True),
adjusted.integral())
# first bin (x_min) should contain all events
# with x <= x_min
x_min_bin = self.simple.FindBin(x_min)
x_max_bin = self.simple.FindBin(x_max)
self.assertEqual(self.simple.integral(0, x_min_bin),
adjusted.GetBinContent(x_min_bin))
# last bin (x_max) should contain all events
# with x >= x_max
self.assertEqual(
self.simple.integral(x_max_bin,
self.simple.nbins() + 1),
adjusted.GetBinContent(x_max_bin))
