Module flavio.test_functions
Classes
class TestFunctions (methodName='runTest')-
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class TestFunctions(unittest.TestCase): def test_functions(self): o = Observable('test_obs') o.arguments = ['x'] def f(wc_obj, par_dict, x): return x pr = Prediction('test_obs', f ) wc_obj = None self.assertEqual(flavio.sm_prediction('test_obs', 7), 7) self.assertEqual(flavio.np_prediction('test_obs', x=7, wc_obj=wc_obj), 7) self.assertEqual(flavio.sm_uncertainty('test_obs', 7), 0) self.assertEqual(flavio.np_uncertainty('test_obs', x=7, wc_obj=wc_obj), 0) self.assertEqual(flavio.sm_uncertainty('test_obs', 7, threads=2), 0) self.assertEqual(flavio.np_uncertainty('test_obs', x=7, wc_obj=wc_obj, threads=2), 0) # delete dummy instance Observable.del_instance('test_obs') def test_exp_combo(self): o = Observable('test_obs') o.arguments = ['x'] m = Measurement('test_obs measurement 1') m.add_constraint([('test_obs', 1)], MultivariateNormalDistribution([1, 2], np.eye(2))) # error: no measurement with self.assertRaises(ValueError): flavio.combine_measurements('test_obs', x=1, include_measurements=['bla']) m.add_constraint([('test_obs', 1)], NormalDistribution(2, 3)) combo = flavio.combine_measurements('test_obs', x=1) self.assertEqual(combo.central_value, 2) self.assertEqual(combo.standard_deviation, 3) m2 = Measurement('test_obs measurement 2') m2.add_constraint([('test_obs', 1)], NormalDistribution(3, 3)) combo = flavio.combine_measurements('test_obs', x=1) self.assertAlmostEqual(combo.central_value, 2.5) self.assertAlmostEqual(combo.standard_deviation, sqrt(9 / 2)) Observable.del_instance('test_obs') def test_get_dep_par(self): self.assertEqual( get_dependent_parameters_sm('BR(Bs->mumu)'), {'DeltaGamma/Gamma_Bs', 'GF', 'Vcb', 'Vub', 'Vus', 'alpha_e', 'alpha_s', 'f_Bs', 'gamma', 'm_Bs', 'm_b', 'm_mu', 'm_s', 'tau_Bs', 'm_t'} ) self.assertEqual( get_dependent_parameters_sm('BR(B0->ee)'), {'GF', 'Vcb', 'Vub', 'Vus', 'alpha_e', 'alpha_s', 'f_B0', 'gamma', 'm_B0', 'm_b', 'm_e', 'm_d', 'tau_B0', 'm_t'} ) # for more complicated cases, just check there is no error get_dependent_parameters_sm('dBR/dq2(B+->Kmumu)', 3) get_dependent_parameters_sm('<dBR/dq2>(B+->Kmumu)', 3, 5) get_dependent_parameters_sm('dBR/dq2(B+->Kmumu)', q2=3) get_dependent_parameters_sm('<dBR/dq2>(B+->Kmumu)', q2min=3, q2max=5) def test_sm_covariance(self): o1 = Observable( 'test_obs 1' ) o2 = Observable( 'test_obs 2' ) def f1(wc_obj, par_dict): return par_dict['m_b'] def f2(wc_obj, par_dict): return par_dict['m_c'] Prediction('test_obs 1', f1) Prediction('test_obs 2', f2) cov_par = np.array([[0.1**2, 0.1*0.2*0.3], [0.1*0.2*0.3, 0.2**2]]) d = flavio.statistics.probability.MultivariateNormalDistribution([4.2, 1.2], covariance=cov_par) par = copy.deepcopy(flavio.parameters.default_parameters) par.add_constraint(['m_b', 'm_c'], d) # test serial np.random.seed(135) cov = flavio.sm_covariance(['test_obs 1', 'test_obs 2'], N=1000, par_vary='all', par_obj=par) npt.assert_array_almost_equal(cov, cov_par, decimal=2) # test parallel np.random.seed(135) cov_parallel = flavio.sm_covariance(['test_obs 1', 'test_obs 2'], N=1000, par_vary='all', par_obj=par, threads=4) npt.assert_array_almost_equal(cov, cov_parallel, decimal=6) np.random.seed(135) cov_1 = flavio.sm_covariance(['test_obs 1'], N=1000, par_vary='all', par_obj=par) # test with single observable npt.assert_array_almost_equal(cov_1, cov[0, 0]) # test with fixed parameter cov_f = flavio.sm_covariance(['test_obs 1', 'test_obs 2'], N=1000, par_vary=['m_b'], par_obj=par) npt.assert_array_almost_equal(cov_f, [[cov_par[0, 0], 0], [0, 0]], decimal=3) # delete dummy instances Observable.del_instance('test_obs 1') Observable.del_instance('test_obs 2') def test_dependent_wcs(self): # WET self.assertEqual( get_dependent_wcs('DeltaM_d'), {(4.2, 'WET', 'flavio', ('dbdb',))} ) # WET-3 self.assertEqual( get_dependent_wcs('eps_K'), {(2.0, 'WET-3', 'flavio', ('sdsd',))} ) self.assertEqual( get_dependent_wcs('BR(K+->pinunu)'), {(2.0, 'WET-3', 'flavio', ('sdnunu',))} ) # SMEFT self.assertEqual( get_dependent_wcs('AFB(Z->bb)'), {(91.1876, 'SMEFT', 'Warsaw', 'all')} ) # something with G_F self.assertEqual( get_dependent_wcs('BR(tau->enunu)'), {(1.0, 'WET-3', 'flavio', ('nunumue',)), (1.8, 'WET-4', 'flavio', ('nunutaue',))} )A class whose instances are single test cases.
By default, the test code itself should be placed in a method named 'runTest'.
If the fixture may be used for many test cases, create as many test methods as are needed. When instantiating such a TestCase subclass, specify in the constructor arguments the name of the test method that the instance is to execute.
Test authors should subclass TestCase for their own tests. Construction and deconstruction of the test's environment ('fixture') can be implemented by overriding the 'setUp' and 'tearDown' methods respectively.
If it is necessary to override the init method, the base class init method must always be called. It is important that subclasses should not change the signature of their init method, since instances of the classes are instantiated automatically by parts of the framework in order to be run.
When subclassing TestCase, you can set these attributes: * failureException: determines which exception will be raised when the instance's assertion methods fail; test methods raising this exception will be deemed to have 'failed' rather than 'errored'. * longMessage: determines whether long messages (including repr of objects used in assert methods) will be printed on failure in addition to any explicit message passed. * maxDiff: sets the maximum length of a diff in failure messages by assert methods using difflib. It is looked up as an instance attribute so can be configured by individual tests if required.
Create an instance of the class that will use the named test method when executed. Raises a ValueError if the instance does not have a method with the specified name.
Ancestors
- unittest.case.TestCase
Methods
def test_dependent_wcs(self)-
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def test_dependent_wcs(self): # WET self.assertEqual( get_dependent_wcs('DeltaM_d'), {(4.2, 'WET', 'flavio', ('dbdb',))} ) # WET-3 self.assertEqual( get_dependent_wcs('eps_K'), {(2.0, 'WET-3', 'flavio', ('sdsd',))} ) self.assertEqual( get_dependent_wcs('BR(K+->pinunu)'), {(2.0, 'WET-3', 'flavio', ('sdnunu',))} ) # SMEFT self.assertEqual( get_dependent_wcs('AFB(Z->bb)'), {(91.1876, 'SMEFT', 'Warsaw', 'all')} ) # something with G_F self.assertEqual( get_dependent_wcs('BR(tau->enunu)'), {(1.0, 'WET-3', 'flavio', ('nunumue',)), (1.8, 'WET-4', 'flavio', ('nunutaue',))} ) def test_exp_combo(self)-
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def test_exp_combo(self): o = Observable('test_obs') o.arguments = ['x'] m = Measurement('test_obs measurement 1') m.add_constraint([('test_obs', 1)], MultivariateNormalDistribution([1, 2], np.eye(2))) # error: no measurement with self.assertRaises(ValueError): flavio.combine_measurements('test_obs', x=1, include_measurements=['bla']) m.add_constraint([('test_obs', 1)], NormalDistribution(2, 3)) combo = flavio.combine_measurements('test_obs', x=1) self.assertEqual(combo.central_value, 2) self.assertEqual(combo.standard_deviation, 3) m2 = Measurement('test_obs measurement 2') m2.add_constraint([('test_obs', 1)], NormalDistribution(3, 3)) combo = flavio.combine_measurements('test_obs', x=1) self.assertAlmostEqual(combo.central_value, 2.5) self.assertAlmostEqual(combo.standard_deviation, sqrt(9 / 2)) Observable.del_instance('test_obs') def test_functions(self)-
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def test_functions(self): o = Observable('test_obs') o.arguments = ['x'] def f(wc_obj, par_dict, x): return x pr = Prediction('test_obs', f ) wc_obj = None self.assertEqual(flavio.sm_prediction('test_obs', 7), 7) self.assertEqual(flavio.np_prediction('test_obs', x=7, wc_obj=wc_obj), 7) self.assertEqual(flavio.sm_uncertainty('test_obs', 7), 0) self.assertEqual(flavio.np_uncertainty('test_obs', x=7, wc_obj=wc_obj), 0) self.assertEqual(flavio.sm_uncertainty('test_obs', 7, threads=2), 0) self.assertEqual(flavio.np_uncertainty('test_obs', x=7, wc_obj=wc_obj, threads=2), 0) # delete dummy instance Observable.del_instance('test_obs') def test_get_dep_par(self)-
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def test_get_dep_par(self): self.assertEqual( get_dependent_parameters_sm('BR(Bs->mumu)'), {'DeltaGamma/Gamma_Bs', 'GF', 'Vcb', 'Vub', 'Vus', 'alpha_e', 'alpha_s', 'f_Bs', 'gamma', 'm_Bs', 'm_b', 'm_mu', 'm_s', 'tau_Bs', 'm_t'} ) self.assertEqual( get_dependent_parameters_sm('BR(B0->ee)'), {'GF', 'Vcb', 'Vub', 'Vus', 'alpha_e', 'alpha_s', 'f_B0', 'gamma', 'm_B0', 'm_b', 'm_e', 'm_d', 'tau_B0', 'm_t'} ) # for more complicated cases, just check there is no error get_dependent_parameters_sm('dBR/dq2(B+->Kmumu)', 3) get_dependent_parameters_sm('<dBR/dq2>(B+->Kmumu)', 3, 5) get_dependent_parameters_sm('dBR/dq2(B+->Kmumu)', q2=3) get_dependent_parameters_sm('<dBR/dq2>(B+->Kmumu)', q2min=3, q2max=5) def test_sm_covariance(self)-
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def test_sm_covariance(self): o1 = Observable( 'test_obs 1' ) o2 = Observable( 'test_obs 2' ) def f1(wc_obj, par_dict): return par_dict['m_b'] def f2(wc_obj, par_dict): return par_dict['m_c'] Prediction('test_obs 1', f1) Prediction('test_obs 2', f2) cov_par = np.array([[0.1**2, 0.1*0.2*0.3], [0.1*0.2*0.3, 0.2**2]]) d = flavio.statistics.probability.MultivariateNormalDistribution([4.2, 1.2], covariance=cov_par) par = copy.deepcopy(flavio.parameters.default_parameters) par.add_constraint(['m_b', 'm_c'], d) # test serial np.random.seed(135) cov = flavio.sm_covariance(['test_obs 1', 'test_obs 2'], N=1000, par_vary='all', par_obj=par) npt.assert_array_almost_equal(cov, cov_par, decimal=2) # test parallel np.random.seed(135) cov_parallel = flavio.sm_covariance(['test_obs 1', 'test_obs 2'], N=1000, par_vary='all', par_obj=par, threads=4) npt.assert_array_almost_equal(cov, cov_parallel, decimal=6) np.random.seed(135) cov_1 = flavio.sm_covariance(['test_obs 1'], N=1000, par_vary='all', par_obj=par) # test with single observable npt.assert_array_almost_equal(cov_1, cov[0, 0]) # test with fixed parameter cov_f = flavio.sm_covariance(['test_obs 1', 'test_obs 2'], N=1000, par_vary=['m_b'], par_obj=par) npt.assert_array_almost_equal(cov_f, [[cov_par[0, 0], 0], [0, 0]], decimal=3) # delete dummy instances Observable.del_instance('test_obs 1') Observable.del_instance('test_obs 2')