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)
