def setUp(self):
# create histograms
h_bkg1_1 = Hist(100, 40, 200, title='Background')
h_signal_1 = h_bkg1_1.Clone(title='Signal')
h_data_1 = h_bkg1_1.Clone(title='Data')
h_bkg1_2 = h_bkg1_1.Clone(title='Background')
h_signal_2 = h_bkg1_1.Clone(title='Signal')
h_data_2 = h_bkg1_1.Clone(title='Data')
# fill the histograms with our distributions
map(h_bkg1_1.Fill, x1)
map(h_signal_1.Fill, x2)
map(h_data_1.Fill, x1_obs)
map(h_data_1.Fill, x2_obs)
map(h_bkg1_2.Fill, x3)
map(h_signal_2.Fill, x4)
map(h_data_2.Fill, x3_obs)
map(h_data_2.Fill, x4_obs)
h_data_1.Scale(data_scale)
h_data_2.Scale(data_scale)
histograms_1 = {'signal': h_signal_1, 'bkg1': h_bkg1_1}
histograms_2 = {'signal': h_signal_2, 'bkg1': h_bkg1_2}
fit_data_1 = FitData(h_data_1, histograms_1, fit_boundaries=(40, 200))
fit_data_2 = FitData(h_data_2, histograms_2, fit_boundaries=(40, 200))
single_fit_collection = FitDataCollection()
single_fit_collection.add(fit_data_1)
collection_1 = FitDataCollection()
collection_1.add(fit_data_1, 'var1')
collection_1.add(fit_data_2, 'var2')
collection_2 = FitDataCollection()
collection_2.add(fit_data_1, 'var1')
collection_2.add(fit_data_2, 'var2')
collection_2.set_normalisation_constraints({'bkg1': 0.5})
collection_3 = FitDataCollection()
collection_3.add(fit_data_1, 'var1')
collection_3.add(fit_data_2, 'var2')
collection_3.set_normalisation_constraints({'bkg1': 0.001})
self.minuit_fitter = Minuit(single_fit_collection)
self.minuit_fitter.fit()
self.simultaneous_fit = Minuit(collection_1)
self.simultaneous_fit.fit()
self.simultaneous_fit_with_constraints = Minuit(collection_2)
self.simultaneous_fit_with_constraints.fit()
self.simultaneous_fit_with_bad_constraints = Minuit(collection_3)
self.simultaneous_fit_with_bad_constraints.fit()
def setUp(self):
# create histograms
h_bkg1_1 = Hist(100, 40, 200, title="Background")
h_signal_1 = h_bkg1_1.Clone(title="Signal")
h_data_1 = h_bkg1_1.Clone(title="Data")
h_bkg1_2 = h_bkg1_1.Clone(title="Background")
h_signal_2 = h_bkg1_1.Clone(title="Signal")
h_data_2 = h_bkg1_1.Clone(title="Data")
# fill the histograms with our distributions
map(h_bkg1_1.Fill, x1)
map(h_signal_1.Fill, x2)
map(h_data_1.Fill, x1_obs)
map(h_data_1.Fill, x2_obs)
map(h_bkg1_2.Fill, x3)
map(h_signal_2.Fill, x4)
map(h_data_2.Fill, x3_obs)
map(h_data_2.Fill, x4_obs)
h_data_1.Scale(data_scale)
h_data_2.Scale(data_scale)
histograms_1 = {"signal": h_signal_1, "bkg1": h_bkg1_1}
histograms_2 = {"signal": h_signal_2, "bkg1": h_bkg1_2}
fit_data_1 = FitData(h_data_1, histograms_1, fit_boundaries=(40, 200))
fit_data_2 = FitData(h_data_2, histograms_2, fit_boundaries=(40, 200))
single_fit_collection = FitDataCollection()
single_fit_collection.add(fit_data_1)
collection_1 = FitDataCollection()
collection_1.add(fit_data_1, "var1")
collection_1.add(fit_data_2, "var2")
collection_2 = FitDataCollection()
collection_2.add(fit_data_1, "var1")
collection_2.add(fit_data_2, "var2")
collection_2.set_normalisation_constraints({"bkg1": 0.5})
collection_3 = FitDataCollection()
collection_3.add(fit_data_1, "var1")
collection_3.add(fit_data_2, "var2")
collection_3.set_normalisation_constraints({"bkg1": 0.001})
self.minuit_fitter = Minuit(single_fit_collection)
self.minuit_fitter.fit()
self.simultaneous_fit = Minuit(collection_1)
self.simultaneous_fit.fit()
self.simultaneous_fit_with_constraints = Minuit(collection_2)
self.simultaneous_fit_with_constraints.fit()
self.simultaneous_fit_with_bad_constraints = Minuit(collection_3)
self.simultaneous_fit_with_bad_constraints.fit()
class Test(unittest.TestCase):
def setUp(self):
# create histograms
h_bkg1_1 = Hist(100, 40, 200, title='Background')
h_signal_1 = h_bkg1_1.Clone(title='Signal')
h_data_1 = h_bkg1_1.Clone(title='Data')
# fill the histograms with our distributions
map(h_bkg1_1.Fill, x1)
map(h_signal_1.Fill, x2)
map(h_data_1.Fill, x1_obs)
map(h_data_1.Fill, x2_obs)
histograms_1 = {'signal': h_signal_1,
'bkg1': h_bkg1_1,
# 'data': h_data_1
}
fit_data_1 = FitData(h_data_1, histograms_1, fit_boundaries=(40, 200))
self.single_fit_collection = FitDataCollection()
self.single_fit_collection.add( fit_data_1 )
# self.roofitFitter = RooFitFit(histograms_1, dataLabel='data', fit_boundries=(40, 200))
self.roofitFitter = RooFitFit(self.single_fit_collection)
def tearDown(self):
pass
def test_normalisation(self):
normalisation = self.roofitFitter.normalisation
self.assertAlmostEqual(normalisation["data"], N_data, delta=sqrt(N_data))
self.assertAlmostEqual(normalisation["bkg1"], N_bkg1, delta=sqrt(N_bkg1))
self.assertAlmostEqual(normalisation["signal"], N_signal, delta=sqrt(N_signal))
def test_signal_result(self):
self.roofitFitter.fit()
results = self.roofitFitter.readResults()
self.assertAlmostEqual(N_signal_obs, results['signal'][0], delta=2 * results['signal'][1])
self.assertAlmostEqual(N_bkg1_obs, results['bkg1'][0], delta=2 * results['bkg1'][1])
def test_constraints(self):
self.single_fit_collection.set_normalisation_constraints({'signal': 0.8, 'bkg1': 0.5})
self.roofitFitter = RooFitFit(self.single_fit_collection)
# self.roofitFitter.set_fit_constraints({'signal': 0.8, 'bkg1': 0.5})
self.roofitFitter.fit()
results = self.roofitFitter.readResults()
self.assertAlmostEqual(N_signal_obs, results['signal'][0], delta=2 * results['signal'][1])
self.assertAlmostEqual(N_bkg1_obs, results['bkg1'][0], delta=2 * results['bkg1'][1])
class Test( unittest.TestCase ):
def setUp( self ):
# create histograms
h_bkg1_1 = Hist( 100, 40, 200, title = 'Background' )
h_signal_1 = h_bkg1_1.Clone( title = 'Signal' )
h_data_1 = h_bkg1_1.Clone( title = 'Data' )
h_bkg1_2 = h_bkg1_1.Clone( title = 'Background' )
h_signal_2 = h_bkg1_1.Clone( title = 'Signal' )
h_data_2 = h_bkg1_1.Clone( title = 'Data' )
# fill the histograms with our distributions
map( h_bkg1_1.Fill, x1 )
map( h_signal_1.Fill, x2 )
map( h_data_1.Fill, x1_obs )
map( h_data_1.Fill, x2_obs )
map( h_bkg1_2.Fill, x3 )
map( h_signal_2.Fill, x4 )
map( h_data_2.Fill, x3_obs )
map( h_data_2.Fill, x4_obs )
h_data_1.Scale(data_scale)
h_data_2.Scale(data_scale)
self.histograms_1 = {'signal': h_signal_1,
'bkg1': h_bkg1_1}
self.histograms_2 = {'signal': h_signal_2,
'bkg1': h_bkg1_2}
self.histograms_3 = {'var1': h_signal_1,
'bkg1': h_bkg1_1}
self.fit_data_1 = FitData( h_data_1, self.histograms_1, fit_boundaries = ( x_min, x_max ))
self.fit_data_2 = FitData( h_data_2, self.histograms_2, fit_boundaries = ( x_min, x_max ))
self.fit_data_3 = FitData( h_data_1, self.histograms_3, fit_boundaries = ( x_min, x_max ))
self.collection_1 = FitDataCollection()
self.collection_1.add( self.fit_data_1, 'signal region' )
self.collection_1.add( self.fit_data_2, 'control region' )
self.collection_1.set_normalisation_constraints({'bkg1': 0.5})
self.collection_2 = FitDataCollection()
self.collection_2.add( self.fit_data_1 )
self.collection_2.add( self.fit_data_2 )
self.collection_2.set_normalisation_constraints({'bkg1': 0.5})
self.single_collection = FitDataCollection()
self.single_collection.add( self.fit_data_1 )
self.single_collection.set_normalisation_constraints({'bkg1': 0.5})
self.non_simultaneous_fit_collection = FitDataCollection()
self.non_simultaneous_fit_collection.add( self.fit_data_1 )
self.non_simultaneous_fit_collection.add( self.fit_data_3 )
self.h_data = h_data_1
self.h_bkg1 = h_bkg1_1
self.h_signal = h_signal_1
def tearDown( self ):
pass
def test_is_valid_for_simultaneous_fit( self ):
self.assertTrue( self.collection_1.is_valid_for_simultaneous_fit(), msg = 'has_same_n_samples: ' + str(self.collection_1.has_same_n_samples) + ', has_same_n_data: ' + str(self.collection_1.has_same_n_data) )
self.assertTrue( self.collection_2.is_valid_for_simultaneous_fit(), msg = 'has_same_n_samples: ' + str(self.collection_1.has_same_n_samples) + ', has_same_n_data: ' + str(self.collection_1.has_same_n_data) )
self.assertFalse( self.non_simultaneous_fit_collection.is_valid_for_simultaneous_fit() )
def test_samples( self ):
samples = sorted( self.histograms_1.keys() )
samples_from_fit_data = sorted( self.fit_data_1.samples )
samples_from_fit_data_collection = self.collection_1.mc_samples()
self.assertEqual( samples, samples_from_fit_data )
self.assertEqual( samples, samples_from_fit_data_collection )
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_real_data( self ):
real_data = self.fit_data_1.real_data_histogram()
self.assertEqual( self.h_data.integral( overflow = True ), real_data.Integral() )
def test_overwrite_warning( self ):
c = FitDataCollection()
c.add( self.fit_data_1, 'var1' )
self.assertRaises( UserWarning, c.add, ( self.fit_data_1, 'var1' ) )
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_constraints(self):
constraint_from_single_collection = self.single_collection.constraints()['bkg1']
self.assertEqual(0.5, constraint_from_single_collection)
class Test(unittest.TestCase):
def setUp(self):
# create histograms
h_bkg1_1 = Hist(100, 40, 200, title='Background')
h_signal_1 = h_bkg1_1.Clone(title='Signal')
h_data_1 = h_bkg1_1.Clone(title='Data')
# fill the histograms with our distributions
map(h_bkg1_1.Fill, x1)
map(h_signal_1.Fill, x2)
map(h_data_1.Fill, x1_obs)
map(h_data_1.Fill, x2_obs)
histograms_1 = {
'signal': h_signal_1,
'bkg1': h_bkg1_1,
# 'data': h_data_1
}
fit_data_1 = FitData(h_data_1, histograms_1, fit_boundaries=(40, 200))
self.single_fit_collection = FitDataCollection()
self.single_fit_collection.add(fit_data_1)
# self.roofitFitter = RooFitFit(histograms_1, dataLabel='data', fit_boundries=(40, 200))
self.roofitFitter = RooFitFit(self.single_fit_collection)
def tearDown(self):
pass
def test_normalisation(self):
normalisation = self.roofitFitter.normalisation
self.assertAlmostEqual(normalisation["data"],
N_data,
delta=sqrt(N_data))
self.assertAlmostEqual(normalisation["bkg1"],
N_bkg1,
delta=sqrt(N_bkg1))
self.assertAlmostEqual(normalisation["signal"],
N_signal,
delta=sqrt(N_signal))
def test_signal_result(self):
self.roofitFitter.fit()
results = self.roofitFitter.readResults()
self.assertAlmostEqual(N_signal_obs,
results['signal'][0],
delta=2 * results['signal'][1])
self.assertAlmostEqual(N_bkg1_obs,
results['bkg1'][0],
delta=2 * results['bkg1'][1])
def test_constraints(self):
self.single_fit_collection.set_normalisation_constraints({
'signal': 0.8,
'bkg1': 0.5
})
self.roofitFitter = RooFitFit(self.single_fit_collection)
# self.roofitFitter.set_fit_constraints({'signal': 0.8, 'bkg1': 0.5})
self.roofitFitter.fit()
results = self.roofitFitter.readResults()
self.assertAlmostEqual(N_signal_obs,
results['signal'][0],
delta=2 * results['signal'][1])
self.assertAlmostEqual(N_bkg1_obs,
results['bkg1'][0],
delta=2 * results['bkg1'][1])
class Test(unittest.TestCase):
def setUp(self):
# create histograms
h_bkg1_1 = Hist(100, 40, 200, title='Background')
h_signal_1 = h_bkg1_1.Clone(title='Signal')
h_data_1 = h_bkg1_1.Clone(title='Data')
h_bkg1_2 = h_bkg1_1.Clone(title='Background')
h_signal_2 = h_bkg1_1.Clone(title='Signal')
h_data_2 = h_bkg1_1.Clone(title='Data')
# fill the histograms with our distributions
map(h_bkg1_1.Fill, x1)
map(h_signal_1.Fill, x2)
map(h_data_1.Fill, x1_obs)
map(h_data_1.Fill, x2_obs)
map(h_bkg1_2.Fill, x3)
map(h_signal_2.Fill, x4)
map(h_data_2.Fill, x3_obs)
map(h_data_2.Fill, x4_obs)
h_data_1.Scale(data_scale)
h_data_2.Scale(data_scale)
self.histograms_1 = {'signal': h_signal_1, 'bkg1': h_bkg1_1}
self.histograms_2 = {'signal': h_signal_2, 'bkg1': h_bkg1_2}
self.histograms_3 = {'var1': h_signal_1, 'bkg1': h_bkg1_1}
self.fit_data_1 = FitData(h_data_1,
self.histograms_1,
fit_boundaries=(x_min, x_max))
self.fit_data_2 = FitData(h_data_2,
self.histograms_2,
fit_boundaries=(x_min, x_max))
self.fit_data_3 = FitData(h_data_1,
self.histograms_3,
fit_boundaries=(x_min, x_max))
self.collection_1 = FitDataCollection()
self.collection_1.add(self.fit_data_1, 'signal region')
self.collection_1.add(self.fit_data_2, 'control region')
self.collection_1.set_normalisation_constraints({'bkg1': 0.5})
self.collection_2 = FitDataCollection()
self.collection_2.add(self.fit_data_1)
self.collection_2.add(self.fit_data_2)
self.collection_2.set_normalisation_constraints({'bkg1': 0.5})
self.single_collection = FitDataCollection()
self.single_collection.add(self.fit_data_1)
self.single_collection.set_normalisation_constraints({'bkg1': 0.5})
self.non_simultaneous_fit_collection = FitDataCollection()
self.non_simultaneous_fit_collection.add(self.fit_data_1)
self.non_simultaneous_fit_collection.add(self.fit_data_3)
self.h_data = h_data_1
self.h_bkg1 = h_bkg1_1
self.h_signal = h_signal_1
def tearDown(self):
pass
def test_is_valid_for_simultaneous_fit(self):
self.assertTrue(self.collection_1.is_valid_for_simultaneous_fit(),
msg='has_same_n_samples: ' +
str(self.collection_1.has_same_n_samples) +
', has_same_n_data: ' +
str(self.collection_1.has_same_n_data))
self.assertTrue(self.collection_2.is_valid_for_simultaneous_fit(),
msg='has_same_n_samples: ' +
str(self.collection_1.has_same_n_samples) +
', has_same_n_data: ' +
str(self.collection_1.has_same_n_data))
self.assertFalse(self.non_simultaneous_fit_collection.
is_valid_for_simultaneous_fit())
def test_samples(self):
samples = sorted(self.histograms_1.keys())
samples_from_fit_data = sorted(self.fit_data_1.samples)
samples_from_fit_data_collection = self.collection_1.mc_samples()
self.assertEqual(samples, samples_from_fit_data)
self.assertEqual(samples, samples_from_fit_data_collection)
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_real_data(self):
real_data = self.fit_data_1.real_data_histogram()
self.assertEqual(self.h_data.integral(overflow=True),
real_data.Integral())
def test_overwrite_warning(self):
c = FitDataCollection()
c.add(self.fit_data_1, 'var1')
self.assertRaises(UserWarning, c.add, (self.fit_data_1, 'var1'))
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_constraints(self):
constraint_from_single_collection = self.single_collection.constraints(
)['bkg1']
self.assertEqual(0.5, constraint_from_single_collection)
