Module flavio.statistics.test_likelihood

Classes

class TestCovariances (methodName='runTest')

Expand source code

class TestCovariances(unittest.TestCase):
    def test_sm_covariance(self):
        # dummy observables
        o1 = Observable( 'test_obs 1' )
        o2 = Observable( 'test_obs 2' )
        # dummy predictions
        def f1(wc_obj, par_dict):
            return par_dict['m_b']
        def f2(wc_obj, par_dict):
            return 2.5
        Prediction( 'test_obs 1', f1 )
        Prediction( 'test_obs 2', f2 )
        fit2 = SMCovariance(['test_obs 1', 'test_obs 2'], vary_parameters=['m_b'])
        # single observable
        fit1 = SMCovariance(['test_obs 1'], vary_parameters=['m_b'])
        for fit in (fit2, fit1):
            fit.get()
            cov_before = fit._cov
            filename = os.path.join(tempfile.gettempdir(), 'tmp-no-p')
            fit.save(filename)
            fit.load(filename)
            cov_after = fit._cov
            npt.assert_array_equal(cov_before, cov_after)
            os.remove(filename)
            filename = os.path.join(tempfile.gettempdir(), 'tmp.p')
            fit.save(filename)
            fit.load(filename)
            cov_after = fit._cov
            npt.assert_array_equal(cov_before, cov_after)
            os.remove(filename)
        # removing dummy instances
        Observable.del_instance('test_obs 1')
        Observable.del_instance('test_obs 2')

    def test_exp_covariance(self):
        # dummy observables
        o1 = Observable( 'test_obs 1' )
        o2 = Observable( 'test_obs 2' )
        # dummy predictions
        def f1(wc_obj, par_dict):
            return par_dict['m_b']
        def f2(wc_obj, par_dict):
            return 2.5
        Prediction( 'test_obs 1', f1 )
        Prediction( 'test_obs 2', f2 )
        d1 = NormalDistribution(5, 0.2)
        cov2 = [[0.1**2, 0.5*0.1*0.3], [0.5*0.1*0.3, 0.3**2]]
        d2 = MultivariateNormalDistribution([6,2], cov2)
        m1 = Measurement( 'measurement 1 of test_obs 1' )
        m2 = Measurement( 'measurement 2 of test_obs 1 and test_obs 2' )
        m1.add_constraint(['test_obs 1'], d1)
        m2.add_constraint(['test_obs 1', 'test_obs 2'], d2)
        fit2 = MeasurementCovariance(MeasurementLikelihood(['test_obs 1', 'test_obs 2']))
        # single observable
        fit1 = MeasurementCovariance(MeasurementLikelihood(['test_obs 1']))
        for fit in (fit2, fit1):
            fit.get()
            cov_before = fit._central_cov[1]
            filename = os.path.join(tempfile.gettempdir(), 'tmp-no-p')
            fit.save(filename)
            fit.load(filename)
            cov_after = fit._central_cov[1]
            npt.assert_array_equal(cov_before, cov_after)
            os.remove(filename)
            filename = os.path.join(tempfile.gettempdir(), 'tmp.p')
            fit.save(filename)
            fit.load(filename)
            cov_after = fit._central_cov[1]
            npt.assert_array_equal(cov_before, cov_after)
            os.remove(filename)
        # removing dummy instances
        Observable.del_instance('test_obs 1')
        Observable.del_instance('test_obs 2')
        Measurement.del_instance('measurement 1 of test_obs 1')
        Measurement.del_instance('measurement 2 of test_obs 1 and test_obs 2')

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_exp_covariance(self)

Expand source code

def test_exp_covariance(self):
    # dummy observables
    o1 = Observable( 'test_obs 1' )
    o2 = Observable( 'test_obs 2' )
    # dummy predictions
    def f1(wc_obj, par_dict):
        return par_dict['m_b']
    def f2(wc_obj, par_dict):
        return 2.5
    Prediction( 'test_obs 1', f1 )
    Prediction( 'test_obs 2', f2 )
    d1 = NormalDistribution(5, 0.2)
    cov2 = [[0.1**2, 0.5*0.1*0.3], [0.5*0.1*0.3, 0.3**2]]
    d2 = MultivariateNormalDistribution([6,2], cov2)
    m1 = Measurement( 'measurement 1 of test_obs 1' )
    m2 = Measurement( 'measurement 2 of test_obs 1 and test_obs 2' )
    m1.add_constraint(['test_obs 1'], d1)
    m2.add_constraint(['test_obs 1', 'test_obs 2'], d2)
    fit2 = MeasurementCovariance(MeasurementLikelihood(['test_obs 1', 'test_obs 2']))
    # single observable
    fit1 = MeasurementCovariance(MeasurementLikelihood(['test_obs 1']))
    for fit in (fit2, fit1):
        fit.get()
        cov_before = fit._central_cov[1]
        filename = os.path.join(tempfile.gettempdir(), 'tmp-no-p')
        fit.save(filename)
        fit.load(filename)
        cov_after = fit._central_cov[1]
        npt.assert_array_equal(cov_before, cov_after)
        os.remove(filename)
        filename = os.path.join(tempfile.gettempdir(), 'tmp.p')
        fit.save(filename)
        fit.load(filename)
        cov_after = fit._central_cov[1]
        npt.assert_array_equal(cov_before, cov_after)
        os.remove(filename)
    # removing dummy instances
    Observable.del_instance('test_obs 1')
    Observable.del_instance('test_obs 2')
    Measurement.del_instance('measurement 1 of test_obs 1')
    Measurement.del_instance('measurement 2 of test_obs 1 and test_obs 2')

def test_sm_covariance(self)

Expand source code

def test_sm_covariance(self):
    # dummy observables
    o1 = Observable( 'test_obs 1' )
    o2 = Observable( 'test_obs 2' )
    # dummy predictions
    def f1(wc_obj, par_dict):
        return par_dict['m_b']
    def f2(wc_obj, par_dict):
        return 2.5
    Prediction( 'test_obs 1', f1 )
    Prediction( 'test_obs 2', f2 )
    fit2 = SMCovariance(['test_obs 1', 'test_obs 2'], vary_parameters=['m_b'])
    # single observable
    fit1 = SMCovariance(['test_obs 1'], vary_parameters=['m_b'])
    for fit in (fit2, fit1):
        fit.get()
        cov_before = fit._cov
        filename = os.path.join(tempfile.gettempdir(), 'tmp-no-p')
        fit.save(filename)
        fit.load(filename)
        cov_after = fit._cov
        npt.assert_array_equal(cov_before, cov_after)
        os.remove(filename)
        filename = os.path.join(tempfile.gettempdir(), 'tmp.p')
        fit.save(filename)
        fit.load(filename)
        cov_after = fit._cov
        npt.assert_array_equal(cov_before, cov_after)
        os.remove(filename)
    # removing dummy instances
    Observable.del_instance('test_obs 1')
    Observable.del_instance('test_obs 2')

class TestFastLikelihood (methodName='runTest')

Expand source code

class TestFastLikelihood(unittest.TestCase):

    def test_fastlikelihood(self):
        # dummy observables
        o1 = Observable( 'test_obs 1' )
        o2 = Observable( 'test_obs 2' )
        # dummy predictions
        def f1(wc_obj, par_dict):
            return par_dict['m_b']
        def f2(wc_obj, par_dict):
            return 2.5
        Prediction( 'test_obs 1', f1 )
        Prediction( 'test_obs 2', f2 )
        d1 = NormalDistribution(5, 0.2)
        cov2 = [[0.1**2, 0.5*0.1*0.3], [0.5*0.1*0.3, 0.3**2]]
        d2 = MultivariateNormalDistribution([6,2], cov2)
        m1 = Measurement( 'measurement 1 of test_obs 1' )
        m2 = Measurement( 'measurement 2 of test_obs 1 and test_obs 2' )
        m1.add_constraint(['test_obs 1'], d1)
        m2.add_constraint(['test_obs 1', 'test_obs 2'], d2)
        fit2 = FastLikelihood('fastlh_test_2', flavio.default_parameters, [], ['m_b'], ['test_obs 1', 'test_obs 2'])
        # fit with only a single observable and measurement
        fit1 = FastLikelihood('fastlh_test_1', flavio.default_parameters, [], ['m_b'], ['test_obs 2',])
        fit3 = FastLikelihood('fastlh_test_3', flavio.default_parameters, ['m_b'],  [], ['test_obs 1', 'test_obs 2'],
                       include_measurements=['measurement 2 of test_obs 1 and test_obs 2'])
        for fit in (fit2, fit1):
            fit.make_measurement(N=10000)
        fit = fit2  # the following is only for fit2
        cov_weighted = [[0.008, 0.012], [0.012, 0.0855]]
        mean_weighted = [5.8, 1.7]
        exact_log_likelihood = scipy.stats.multivariate_normal.logpdf([5.9, 2.5], mean_weighted, cov_weighted)
        self.assertAlmostEqual(fit.log_likelihood({'m_b': 5.9}, None), exact_log_likelihood, delta=0.8)
        # self.assertAlmostEqual(fit.best_fit()['x'], 5.9, delta=0.1)
        # removing dummy instances
        FastLikelihood.del_instance('fastlh_test_1')
        FastLikelihood.del_instance('fastlh_test_2')
        Observable.del_instance('test_obs 1')
        Observable.del_instance('test_obs 2')
        Measurement.del_instance('measurement 1 of test_obs 1')
        Measurement.del_instance('measurement 2 of test_obs 1 and test_obs 2')

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_fastlikelihood(self)

Expand source code

def test_fastlikelihood(self):
    # dummy observables
    o1 = Observable( 'test_obs 1' )
    o2 = Observable( 'test_obs 2' )
    # dummy predictions
    def f1(wc_obj, par_dict):
        return par_dict['m_b']
    def f2(wc_obj, par_dict):
        return 2.5
    Prediction( 'test_obs 1', f1 )
    Prediction( 'test_obs 2', f2 )
    d1 = NormalDistribution(5, 0.2)
    cov2 = [[0.1**2, 0.5*0.1*0.3], [0.5*0.1*0.3, 0.3**2]]
    d2 = MultivariateNormalDistribution([6,2], cov2)
    m1 = Measurement( 'measurement 1 of test_obs 1' )
    m2 = Measurement( 'measurement 2 of test_obs 1 and test_obs 2' )
    m1.add_constraint(['test_obs 1'], d1)
    m2.add_constraint(['test_obs 1', 'test_obs 2'], d2)
    fit2 = FastLikelihood('fastlh_test_2', flavio.default_parameters, [], ['m_b'], ['test_obs 1', 'test_obs 2'])
    # fit with only a single observable and measurement
    fit1 = FastLikelihood('fastlh_test_1', flavio.default_parameters, [], ['m_b'], ['test_obs 2',])
    fit3 = FastLikelihood('fastlh_test_3', flavio.default_parameters, ['m_b'],  [], ['test_obs 1', 'test_obs 2'],
                   include_measurements=['measurement 2 of test_obs 1 and test_obs 2'])
    for fit in (fit2, fit1):
        fit.make_measurement(N=10000)
    fit = fit2  # the following is only for fit2
    cov_weighted = [[0.008, 0.012], [0.012, 0.0855]]
    mean_weighted = [5.8, 1.7]
    exact_log_likelihood = scipy.stats.multivariate_normal.logpdf([5.9, 2.5], mean_weighted, cov_weighted)
    self.assertAlmostEqual(fit.log_likelihood({'m_b': 5.9}, None), exact_log_likelihood, delta=0.8)
    # self.assertAlmostEqual(fit.best_fit()['x'], 5.9, delta=0.1)
    # removing dummy instances
    FastLikelihood.del_instance('fastlh_test_1')
    FastLikelihood.del_instance('fastlh_test_2')
    Observable.del_instance('test_obs 1')
    Observable.del_instance('test_obs 2')
    Measurement.del_instance('measurement 1 of test_obs 1')
    Measurement.del_instance('measurement 2 of test_obs 1 and test_obs 2')

class TestLikelihood (methodName='runTest')

Expand source code

class TestLikelihood(unittest.TestCase):
    def test_likelihood(self):
        o = Observable( 'test_obs' )
        def f(wc_obj, par_dict):
            return par_dict['m_b']*2
        Prediction('test_obs', f)
        d = NormalDistribution(4.2, 0.2)
        m = Measurement( 'measurement of test_obs' )
        m.add_constraint(['test_obs'], d)
        par = copy.deepcopy(flavio.parameters.default_parameters)
        par.set_constraint('m_b', '4.2+-0.2')
        par.set_constraint('m_c', '1.2+-0.1')
        pl = Likelihood(par, ['m_b', 'm_c'], ['test_obs'])
        # npt.assert_array_equal(pl.get_central, [4.2, 1.2])
        # self.assertEqual(len(pl.get_random), 2)
        # test likelihoods
        chi2_central = -2 * pl.log_prior_fit_parameters({'m_b': 4.2, 'm_c': 1.2})
        chi2_2s = -2 * pl.log_prior_fit_parameters({'m_b': 4.6, 'm_c': 1.2})
        self.assertAlmostEqual(chi2_2s - chi2_central, 4)
        chi2_central = -2 * pl.log_prior_fit_parameters({'m_b': 4.2, 'm_c': 1.2})
        chi2_2s = -2 * pl.log_prior_fit_parameters({'m_b': 4.2, 'm_c': 1.0})
        self.assertAlmostEqual(chi2_2s - chi2_central, 4)
        Observable.del_instance('test_obs')
        Measurement.del_instance('measurement of test_obs')

    def test_load(self):
        o = Observable( 'test_obs' )
        def f(wc_obj, par_dict):
            return par_dict['m_b']*2
        Prediction('test_obs', f)
        d = NormalDistribution(4.2, 0.2)
        m = Measurement( 'measurement of test_obs' )
        m.add_constraint(['test_obs'], d)
        par = copy.deepcopy(flavio.parameters.default_parameters)
        par.set_constraint('m_b', '4.2+-0.2')
        par.set_constraint('m_c', '1.2+-0.1')
        pl = Likelihood.load_dict({'par_obj': [{'m_b': '4.2+-0.2'},
                                               {'m_c': '1.2+-0.1'}],
                                   'fit_parameters': ['m_b', 'm_c'],
                                   'observables': ['test_obs']})
        pl = Likelihood(par, ['m_b', 'm_c'], ['test_obs'])
        # npt.assert_array_equal(pl.get_central, [4.2, 1.2])
        # self.assertEqual(len(pl.get_random), 2)
        # test likelihoods
        chi2_central = -2 * pl.log_prior_fit_parameters({'m_b': 4.2, 'm_c': 1.2})
        chi2_2s = -2 * pl.log_prior_fit_parameters({'m_b': 4.6, 'm_c': 1.2})
        self.assertAlmostEqual(chi2_2s - chi2_central, 4)
        chi2_central = -2 * pl.log_prior_fit_parameters({'m_b': 4.2, 'm_c': 1.2})
        chi2_2s = -2 * pl.log_prior_fit_parameters({'m_b': 4.2, 'm_c': 1.0})
        self.assertAlmostEqual(chi2_2s - chi2_central, 4)
        Observable.del_instance('test_obs')
        Measurement.del_instance('measurement of test_obs')

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_likelihood(self)

Expand source code

def test_likelihood(self):
    o = Observable( 'test_obs' )
    def f(wc_obj, par_dict):
        return par_dict['m_b']*2
    Prediction('test_obs', f)
    d = NormalDistribution(4.2, 0.2)
    m = Measurement( 'measurement of test_obs' )
    m.add_constraint(['test_obs'], d)
    par = copy.deepcopy(flavio.parameters.default_parameters)
    par.set_constraint('m_b', '4.2+-0.2')
    par.set_constraint('m_c', '1.2+-0.1')
    pl = Likelihood(par, ['m_b', 'm_c'], ['test_obs'])
    # npt.assert_array_equal(pl.get_central, [4.2, 1.2])
    # self.assertEqual(len(pl.get_random), 2)
    # test likelihoods
    chi2_central = -2 * pl.log_prior_fit_parameters({'m_b': 4.2, 'm_c': 1.2})
    chi2_2s = -2 * pl.log_prior_fit_parameters({'m_b': 4.6, 'm_c': 1.2})
    self.assertAlmostEqual(chi2_2s - chi2_central, 4)
    chi2_central = -2 * pl.log_prior_fit_parameters({'m_b': 4.2, 'm_c': 1.2})
    chi2_2s = -2 * pl.log_prior_fit_parameters({'m_b': 4.2, 'm_c': 1.0})
    self.assertAlmostEqual(chi2_2s - chi2_central, 4)
    Observable.del_instance('test_obs')
    Measurement.del_instance('measurement of test_obs')

def test_load(self)

Expand source code

def test_load(self):
    o = Observable( 'test_obs' )
    def f(wc_obj, par_dict):
        return par_dict['m_b']*2
    Prediction('test_obs', f)
    d = NormalDistribution(4.2, 0.2)
    m = Measurement( 'measurement of test_obs' )
    m.add_constraint(['test_obs'], d)
    par = copy.deepcopy(flavio.parameters.default_parameters)
    par.set_constraint('m_b', '4.2+-0.2')
    par.set_constraint('m_c', '1.2+-0.1')
    pl = Likelihood.load_dict({'par_obj': [{'m_b': '4.2+-0.2'},
                                           {'m_c': '1.2+-0.1'}],
                               'fit_parameters': ['m_b', 'm_c'],
                               'observables': ['test_obs']})
    pl = Likelihood(par, ['m_b', 'm_c'], ['test_obs'])
    # npt.assert_array_equal(pl.get_central, [4.2, 1.2])
    # self.assertEqual(len(pl.get_random), 2)
    # test likelihoods
    chi2_central = -2 * pl.log_prior_fit_parameters({'m_b': 4.2, 'm_c': 1.2})
    chi2_2s = -2 * pl.log_prior_fit_parameters({'m_b': 4.6, 'm_c': 1.2})
    self.assertAlmostEqual(chi2_2s - chi2_central, 4)
    chi2_central = -2 * pl.log_prior_fit_parameters({'m_b': 4.2, 'm_c': 1.2})
    chi2_2s = -2 * pl.log_prior_fit_parameters({'m_b': 4.2, 'm_c': 1.0})
    self.assertAlmostEqual(chi2_2s - chi2_central, 4)
    Observable.del_instance('test_obs')
    Measurement.del_instance('measurement of test_obs')

class TestMeasurementLikelihood (methodName='runTest')

Expand source code

class TestMeasurementLikelihood(unittest.TestCase):
    def test_class(self):
        o = Observable( 'test_obs' )
        def f(wc_obj, par_dict):
            return par_dict['m_b']*2
        Prediction('test_obs', f)
        d = NormalDistribution(4.2, 0.2)
        m = Measurement( 'measurement of test_obs' )
        m.add_constraint(['test_obs'], d)
        with self.assertRaises(ValueError):
            # specify include_measurements and exclude_measurements simultaneously
            MeasurementLikelihood(['test_obs'],
                include_measurements=['measurement of test_obs'],
                exclude_measurements=['measurement of test_obs'])
        ml = MeasurementLikelihood(['test_obs'])
        pred = ml.get_predictions_par({'m_b': 4}, None)
        self.assertDictEqual(pred, {'test_obs': 8})
        self.assertEqual(ml.get_measurements, ['measurement of test_obs'])
        Observable.del_instance('test_obs')
        Measurement.del_instance('measurement of test_obs')

    def test_correlation_warning(self):
        o1 = Observable( 'test_obs 1' )
        o2 = Observable( 'test_obs 2' )
        d1 = MultivariateNormalDistribution([1,2],[[1,0],[0,2]])
        d2 = MultivariateNormalDistribution([1,2],[[1,0],[0,2]])
        par = flavio.default_parameters
        m1 = Measurement( '1st measurement of test_obs 1 and 2' )
        m1.add_constraint(['test_obs 1', 'test_obs 2'], d1)
        # this should not prompt a warning
        MeasurementLikelihood(observables=['test_obs 1'])
        m2 = Measurement( '2nd measurement of test_obs 1 and 2' )
        m2.add_constraint(['test_obs 1', 'test_obs 2'], d2)
        # this should now prompt a warning
        with self.assertWarnsRegex(UserWarning,
                                   ".*test_obs 2.*test_obs 1.*"):
            MeasurementLikelihood(observables=['test_obs 1'])
        Observable.del_instance('test_obs 1')
        Observable.del_instance('test_obs 2')
        Measurement.del_instance('1st measurement of test_obs 1 and 2')
        Measurement.del_instance('2nd measurement of test_obs 1 and 2')

    def test_load(self):
        d = {}
        o = Observable( 'test_obs' )
        def f(wc_obj, par_dict):
            return par_dict['m_b']*2
        Prediction('test_obs', f)
        d = NormalDistribution(4.2, 0.2)
        m = Measurement( 'measurement of test_obs' )
        m.add_constraint(['test_obs'], d)
        with self.assertRaises(vol.error.Error):
            # string instead of list
            ml = MeasurementLikelihood.load_dict({'observables' : 'test_obs'})
        with self.assertRaises(TypeError):
            # compulsory argument missing
            ml = MeasurementLikelihood.load_dict({})
        # should work
        ml = MeasurementLikelihood.load_dict({'observables' : ['test_obs']})
        pred = ml.get_predictions_par({'m_b': 4}, None)
        self.assertDictEqual(pred, {'test_obs': 8})
        self.assertEqual(ml.get_measurements, ['measurement of test_obs'])
        self.assertEqual(ml.get_number_observations(), 1)
        m = Measurement( 'measurement 2 of test_obs' )
        m.add_constraint(['test_obs'], d)
        self.assertEqual(ml.get_number_observations(), 2)
        Observable.del_instance('test_obs')
        Measurement.del_instance('measurement of test_obs')
        Measurement.del_instance('measurement 2 of test_obs')

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_class(self)

Expand source code

def test_class(self):
    o = Observable( 'test_obs' )
    def f(wc_obj, par_dict):
        return par_dict['m_b']*2
    Prediction('test_obs', f)
    d = NormalDistribution(4.2, 0.2)
    m = Measurement( 'measurement of test_obs' )
    m.add_constraint(['test_obs'], d)
    with self.assertRaises(ValueError):
        # specify include_measurements and exclude_measurements simultaneously
        MeasurementLikelihood(['test_obs'],
            include_measurements=['measurement of test_obs'],
            exclude_measurements=['measurement of test_obs'])
    ml = MeasurementLikelihood(['test_obs'])
    pred = ml.get_predictions_par({'m_b': 4}, None)
    self.assertDictEqual(pred, {'test_obs': 8})
    self.assertEqual(ml.get_measurements, ['measurement of test_obs'])
    Observable.del_instance('test_obs')
    Measurement.del_instance('measurement of test_obs')

def test_correlation_warning(self)

Expand source code

def test_correlation_warning(self):
    o1 = Observable( 'test_obs 1' )
    o2 = Observable( 'test_obs 2' )
    d1 = MultivariateNormalDistribution([1,2],[[1,0],[0,2]])
    d2 = MultivariateNormalDistribution([1,2],[[1,0],[0,2]])
    par = flavio.default_parameters
    m1 = Measurement( '1st measurement of test_obs 1 and 2' )
    m1.add_constraint(['test_obs 1', 'test_obs 2'], d1)
    # this should not prompt a warning
    MeasurementLikelihood(observables=['test_obs 1'])
    m2 = Measurement( '2nd measurement of test_obs 1 and 2' )
    m2.add_constraint(['test_obs 1', 'test_obs 2'], d2)
    # this should now prompt a warning
    with self.assertWarnsRegex(UserWarning,
                               ".*test_obs 2.*test_obs 1.*"):
        MeasurementLikelihood(observables=['test_obs 1'])
    Observable.del_instance('test_obs 1')
    Observable.del_instance('test_obs 2')
    Measurement.del_instance('1st measurement of test_obs 1 and 2')
    Measurement.del_instance('2nd measurement of test_obs 1 and 2')

def test_load(self)

Expand source code

def test_load(self):
    d = {}
    o = Observable( 'test_obs' )
    def f(wc_obj, par_dict):
        return par_dict['m_b']*2
    Prediction('test_obs', f)
    d = NormalDistribution(4.2, 0.2)
    m = Measurement( 'measurement of test_obs' )
    m.add_constraint(['test_obs'], d)
    with self.assertRaises(vol.error.Error):
        # string instead of list
        ml = MeasurementLikelihood.load_dict({'observables' : 'test_obs'})
    with self.assertRaises(TypeError):
        # compulsory argument missing
        ml = MeasurementLikelihood.load_dict({})
    # should work
    ml = MeasurementLikelihood.load_dict({'observables' : ['test_obs']})
    pred = ml.get_predictions_par({'m_b': 4}, None)
    self.assertDictEqual(pred, {'test_obs': 8})
    self.assertEqual(ml.get_measurements, ['measurement of test_obs'])
    self.assertEqual(ml.get_number_observations(), 1)
    m = Measurement( 'measurement 2 of test_obs' )
    m.add_constraint(['test_obs'], d)
    self.assertEqual(ml.get_number_observations(), 2)
    Observable.del_instance('test_obs')
    Measurement.del_instance('measurement of test_obs')
    Measurement.del_instance('measurement 2 of test_obs')

class TestParameterLikelihood (methodName='runTest')

Expand source code

class TestParameterLikelihood(unittest.TestCase):
    def test_parameter_likelihood(self):
        par = copy.deepcopy(flavio.parameters.default_parameters)
        par.set_constraint('m_b', '4.2+-0.2')
        par.set_constraint('m_c', '1.2+-0.1')
        pl = ParameterLikelihood(par, ['m_b', 'm_c'])
        self.assertListEqual(pl.parameters, ['m_b', 'm_c'])
        npt.assert_array_equal(pl.get_central, [4.2, 1.2])
        self.assertEqual(len(pl.get_random), 2)
        # test likelihood
        chi2_central = -2 * pl.log_likelihood_par({'m_b': 4.2, 'm_c': 1.2})
        chi2_2s = -2 * pl.log_likelihood_par({'m_b': 4.6, 'm_c': 1.0})
        self.assertAlmostEqual(chi2_2s - chi2_central, 4 + 4)


    def test_load(self):
        par = copy.deepcopy(flavio.parameters.default_parameters)
        par.set_constraint('m_b', '4.2+-0.2')
        par.set_constraint('m_c', '1.2+-0.1')
        pl = ParameterLikelihood.load_dict({'par_obj': [{'m_b': '4.2+-0.2'},
                                                   {'m_c': '1.2+-0.1'}],
                                       'parameters': ['m_b', 'm_c']})
        self.assertListEqual(pl.parameters, ['m_b', 'm_c'])
        npt.assert_array_equal(pl.get_central, [4.2, 1.2])
        self.assertEqual(len(pl.get_random), 2)
        # test likelihood
        chi2_central = -2 * pl.log_likelihood_par({'m_b': 4.2, 'm_c': 1.2})
        chi2_2s = -2 * pl.log_likelihood_par({'m_b': 4.6, 'm_c': 1.0})
        self.assertAlmostEqual(chi2_2s - chi2_central, 4 + 4)

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_load(self)

Expand source code

def test_load(self):
    par = copy.deepcopy(flavio.parameters.default_parameters)
    par.set_constraint('m_b', '4.2+-0.2')
    par.set_constraint('m_c', '1.2+-0.1')
    pl = ParameterLikelihood.load_dict({'par_obj': [{'m_b': '4.2+-0.2'},
                                               {'m_c': '1.2+-0.1'}],
                                   'parameters': ['m_b', 'm_c']})
    self.assertListEqual(pl.parameters, ['m_b', 'm_c'])
    npt.assert_array_equal(pl.get_central, [4.2, 1.2])
    self.assertEqual(len(pl.get_random), 2)
    # test likelihood
    chi2_central = -2 * pl.log_likelihood_par({'m_b': 4.2, 'm_c': 1.2})
    chi2_2s = -2 * pl.log_likelihood_par({'m_b': 4.6, 'm_c': 1.0})
    self.assertAlmostEqual(chi2_2s - chi2_central, 4 + 4)

def test_parameter_likelihood(self)

Expand source code

def test_parameter_likelihood(self):
    par = copy.deepcopy(flavio.parameters.default_parameters)
    par.set_constraint('m_b', '4.2+-0.2')
    par.set_constraint('m_c', '1.2+-0.1')
    pl = ParameterLikelihood(par, ['m_b', 'm_c'])
    self.assertListEqual(pl.parameters, ['m_b', 'm_c'])
    npt.assert_array_equal(pl.get_central, [4.2, 1.2])
    self.assertEqual(len(pl.get_random), 2)
    # test likelihood
    chi2_central = -2 * pl.log_likelihood_par({'m_b': 4.2, 'm_c': 1.2})
    chi2_2s = -2 * pl.log_likelihood_par({'m_b': 4.6, 'm_c': 1.0})
    self.assertAlmostEqual(chi2_2s - chi2_central, 4 + 4)