Module flavio.plots.test_plotfunctions
Functions
def dummy_loglikelihood(x)-
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def dummy_loglikelihood(x): return -x[0]**2-x[1]**2
Classes
class TestPlots (methodName='runTest')-
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class TestPlots(unittest.TestCase): def test_error_budget_pie(self): err_budget_bsmumu = {'DeltaGamma/Gamma_Bs': 0.0048858044281356464, 'GF': 1.1082519567930236e-06, 'Vcb': 0.039790267942911725, 'Vub': 0.00039636600693301931, 'Vus': 0.00040185451336679405, 'alpha_e': 0.00017640953398991146, 'alpha_s': 1.7980573055638197e-05, 'f_Bs': 0.035073683628645283, 'gamma': 0.0040950620820486205, 'm_Bs': 4.395718017004336e-05, 'm_mu': 3.89716785433222e-08, 'tau_Bs': 0.003286868163723475} error_budget_pie(err_budget_bsmumu) def test_q2_th_diff(self): # without specifying WCs diff_plot_th('dBR/dq2(B0->pienu)', 0, 25, steps=10, scale_factor=1000) # with WCs diff_plot_th('dBR/dq2(B+->pienu)', 0, 25, wc=flavio.WilsonCoefficients(), steps=10) # check that observable not depending on q2 raises error with self.assertRaises(ValueError): diff_plot_th('eps_K', 0, 25) def test_q2_th_diff_err(self): # without parallelization diff_plot_th_err('dBR/dq2(B0->pienu)', 1, 24, steps=5, steps_err=3, N=10, scale_factor=3) # with parallelization diff_plot_th_err('dBR/dq2(B0->pienu)', 1, 24, steps=5, steps_err=3, N=10, threads=2) def test_q2_th_bin(self): bins = [(0, 5), (5, 10)] # without specifying WCs bin_plot_th('<BR>(B0->pienu)', bins, N=10) # with WCs bin_plot_th('<BR>(B+->pienu)', bins, divide_binwidth=True, wc=flavio.WilsonCoefficients(), N=10) # check that observable not depending on q2 raises error with self.assertRaises(ValueError): bin_plot_th('eps_K', bins) def test_q2_plot_exp(self): # vanilla bin_plot_exp('<dBR/dq2>(B0->K*mumu)') # with options bin_plot_exp('<dBR/dq2>(B0->K*mumu)', col_dict={'LHCb': 'r'}, exclude_bins=[(1.1, 6)], scale_factor=100) # check that observable not depending on q2 raises error with self.assertRaises(ValueError): bin_plot_exp('eps_K') def test_q2_plot_exp(self): # vanilla m = flavio.Measurement('test measurement diff_plot_exp') m.set_constraint(('dBR/dq2(B0->K*mumu)', 1), '1 +- 0.1 e-6') m.set_constraint(('dBR/dq2(B0->K*mumu)', 2), '2 +- 0.2 e-6') diff_plot_exp('dBR/dq2(B0->K*mumu)') diff_plot_exp('dBR/dq2(B0->K*mumu)', scale_factor=10) # with options # check that observable not depending on q2 raises error with self.assertRaises(ValueError): diff_plot_exp('eps_K') # remove test measurement del flavio.Measurement['test measurement diff_plot_exp'] def test_band_plot(self): # NB, this test only runs with matplotlib>=1.5.3 due to a matplotlib bug # check that no error is raised and output dimensions match with warnings.catch_warnings(): warnings.simplefilter("ignore") x, y, z = band_plot(dummy_loglikelihood, -2, 2, -3, 3, steps=30) self.assertEqual(x.shape, (30, 30)) self.assertEqual(y.shape, (30, 30)) self.assertEqual(z.shape, (30, 30)) # with interpolation_factor x, y, z = band_plot(dummy_loglikelihood, -2, 2, -3, 3, steps=30, interpolation_factor=2) self.assertEqual(x.shape, (30, 30)) self.assertEqual(y.shape, (30, 30)) self.assertEqual(z.shape, (30, 30)) # with pre_calculated_z x, y, z = band_plot(None, -2, 2, -3, 3, pre_calculated_z=z, interpolation_factor=2) self.assertEqual(x.shape, (30, 30)) self.assertEqual(y.shape, (30, 30)) self.assertEqual(z.shape, (30, 30)) def test_density_contour_data(self): np.random.seed(42) xy = scipy.stats.multivariate_normal(mean=[2,3], cov=[[1,0.5],[0.5,1]]).rvs(size=100) data = density_contour_data(*xy.T) self.assertEqual(data['x'].shape, (100,100)) self.assertEqual(data['y'].shape, (100,100)) self.assertEqual(data['z'].shape, (100,100)) self.assertEqual(len(data['levels']), 2) # by default 2 levels (1, 2sigma) self.assertTrue(min(data['levels']) > 0) # levels positive self.assertEqual(data['levels'], sorted(data['levels'])) # levels ascending self.assertEqual(np.min(data['z']), 0) # point in the middle should be close to maximum likelihood self.assertAlmostEqual(data['z'][50,50], 0, delta=0.1) # corners self.assertTrue(data['z'][0,0] < data['z'][-1,0]) self.assertTrue(data['z'][-1,-1] < data['z'][0,-1]) # symmetries self.assertAlmostEqual(data['z'][-1,-1], data['z'][0,0], delta=1.) self.assertAlmostEqual(data['z'][-1,0], data['z'][0,-1], delta=3.) def test_density_contour(self): # just check this works np.random.seed(42) xy = scipy.stats.multivariate_normal(mean=[2,3], cov=[[1,0.5],[0.5,1]]).rvs(size=100) density_contour(*xy.T) density_contour_joint(*xy.T) def test_likelihood_contour(self): # just check this works data = likelihood_contour_data(dummy_loglikelihood, -2, 2, -3, 3) self.assertEqual(data['x'].shape, (20,20)) self.assertEqual(data['y'].shape, (20,20)) self.assertEqual(data['z'].shape, (20,20)) self.assertEqual(len(data['levels']), 1) # by default, plot 1 sigma contour self.assertAlmostEqual(data['levels'][0], 2.3, delta=0.01) # self.assertAlmostEqual(np.min(data['z']), 0.07202216) # keep value of mininum # test parallel computation data2 = likelihood_contour_data(dummy_loglikelihood, -2, 2, -3, 3, threads=2) npt.assert_array_equal(data2['z'], data['z']) # check that `z_min` larger than `np.min(z)` raises error with self.assertRaises(ValueError): kwargs = {'z_min':0.1} kwargs.update(data) # since we cannot do **data, **kwargs in Python <3.5 contour(**kwargs) def test_smooth_histogram(self): # just check this doesn't raise and error np.random.seed(42) dat = np.random.normal(117, 23, size=100) smooth_histogram(dat, col=1)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_band_plot(self)-
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def test_band_plot(self): # NB, this test only runs with matplotlib>=1.5.3 due to a matplotlib bug # check that no error is raised and output dimensions match with warnings.catch_warnings(): warnings.simplefilter("ignore") x, y, z = band_plot(dummy_loglikelihood, -2, 2, -3, 3, steps=30) self.assertEqual(x.shape, (30, 30)) self.assertEqual(y.shape, (30, 30)) self.assertEqual(z.shape, (30, 30)) # with interpolation_factor x, y, z = band_plot(dummy_loglikelihood, -2, 2, -3, 3, steps=30, interpolation_factor=2) self.assertEqual(x.shape, (30, 30)) self.assertEqual(y.shape, (30, 30)) self.assertEqual(z.shape, (30, 30)) # with pre_calculated_z x, y, z = band_plot(None, -2, 2, -3, 3, pre_calculated_z=z, interpolation_factor=2) self.assertEqual(x.shape, (30, 30)) self.assertEqual(y.shape, (30, 30)) self.assertEqual(z.shape, (30, 30)) def test_density_contour(self)-
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def test_density_contour(self): # just check this works np.random.seed(42) xy = scipy.stats.multivariate_normal(mean=[2,3], cov=[[1,0.5],[0.5,1]]).rvs(size=100) density_contour(*xy.T) density_contour_joint(*xy.T) def test_density_contour_data(self)-
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def test_density_contour_data(self): np.random.seed(42) xy = scipy.stats.multivariate_normal(mean=[2,3], cov=[[1,0.5],[0.5,1]]).rvs(size=100) data = density_contour_data(*xy.T) self.assertEqual(data['x'].shape, (100,100)) self.assertEqual(data['y'].shape, (100,100)) self.assertEqual(data['z'].shape, (100,100)) self.assertEqual(len(data['levels']), 2) # by default 2 levels (1, 2sigma) self.assertTrue(min(data['levels']) > 0) # levels positive self.assertEqual(data['levels'], sorted(data['levels'])) # levels ascending self.assertEqual(np.min(data['z']), 0) # point in the middle should be close to maximum likelihood self.assertAlmostEqual(data['z'][50,50], 0, delta=0.1) # corners self.assertTrue(data['z'][0,0] < data['z'][-1,0]) self.assertTrue(data['z'][-1,-1] < data['z'][0,-1]) # symmetries self.assertAlmostEqual(data['z'][-1,-1], data['z'][0,0], delta=1.) self.assertAlmostEqual(data['z'][-1,0], data['z'][0,-1], delta=3.) def test_error_budget_pie(self)-
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def test_error_budget_pie(self): err_budget_bsmumu = {'DeltaGamma/Gamma_Bs': 0.0048858044281356464, 'GF': 1.1082519567930236e-06, 'Vcb': 0.039790267942911725, 'Vub': 0.00039636600693301931, 'Vus': 0.00040185451336679405, 'alpha_e': 0.00017640953398991146, 'alpha_s': 1.7980573055638197e-05, 'f_Bs': 0.035073683628645283, 'gamma': 0.0040950620820486205, 'm_Bs': 4.395718017004336e-05, 'm_mu': 3.89716785433222e-08, 'tau_Bs': 0.003286868163723475} error_budget_pie(err_budget_bsmumu) def test_likelihood_contour(self)-
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def test_likelihood_contour(self): # just check this works data = likelihood_contour_data(dummy_loglikelihood, -2, 2, -3, 3) self.assertEqual(data['x'].shape, (20,20)) self.assertEqual(data['y'].shape, (20,20)) self.assertEqual(data['z'].shape, (20,20)) self.assertEqual(len(data['levels']), 1) # by default, plot 1 sigma contour self.assertAlmostEqual(data['levels'][0], 2.3, delta=0.01) # self.assertAlmostEqual(np.min(data['z']), 0.07202216) # keep value of mininum # test parallel computation data2 = likelihood_contour_data(dummy_loglikelihood, -2, 2, -3, 3, threads=2) npt.assert_array_equal(data2['z'], data['z']) # check that `z_min` larger than `np.min(z)` raises error with self.assertRaises(ValueError): kwargs = {'z_min':0.1} kwargs.update(data) # since we cannot do **data, **kwargs in Python <3.5 contour(**kwargs) def test_q2_plot_exp(self)-
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def test_q2_plot_exp(self): # vanilla m = flavio.Measurement('test measurement diff_plot_exp') m.set_constraint(('dBR/dq2(B0->K*mumu)', 1), '1 +- 0.1 e-6') m.set_constraint(('dBR/dq2(B0->K*mumu)', 2), '2 +- 0.2 e-6') diff_plot_exp('dBR/dq2(B0->K*mumu)') diff_plot_exp('dBR/dq2(B0->K*mumu)', scale_factor=10) # with options # check that observable not depending on q2 raises error with self.assertRaises(ValueError): diff_plot_exp('eps_K') # remove test measurement del flavio.Measurement['test measurement diff_plot_exp'] def test_q2_th_bin(self)-
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def test_q2_th_bin(self): bins = [(0, 5), (5, 10)] # without specifying WCs bin_plot_th('<BR>(B0->pienu)', bins, N=10) # with WCs bin_plot_th('<BR>(B+->pienu)', bins, divide_binwidth=True, wc=flavio.WilsonCoefficients(), N=10) # check that observable not depending on q2 raises error with self.assertRaises(ValueError): bin_plot_th('eps_K', bins) def test_q2_th_diff(self)-
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def test_q2_th_diff(self): # without specifying WCs diff_plot_th('dBR/dq2(B0->pienu)', 0, 25, steps=10, scale_factor=1000) # with WCs diff_plot_th('dBR/dq2(B+->pienu)', 0, 25, wc=flavio.WilsonCoefficients(), steps=10) # check that observable not depending on q2 raises error with self.assertRaises(ValueError): diff_plot_th('eps_K', 0, 25) def test_q2_th_diff_err(self)-
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def test_q2_th_diff_err(self): # without parallelization diff_plot_th_err('dBR/dq2(B0->pienu)', 1, 24, steps=5, steps_err=3, N=10, scale_factor=3) # with parallelization diff_plot_th_err('dBR/dq2(B0->pienu)', 1, 24, steps=5, steps_err=3, N=10, threads=2) def test_smooth_histogram(self)-
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def test_smooth_histogram(self): # just check this doesn't raise and error np.random.seed(42) dat = np.random.normal(117, 23, size=100) smooth_histogram(dat, col=1)