def setUp(self): _data = [1, 1, 4, 1, 1, 4, 2, 5, 5, 5, 2, 4, 2, 2, 18, 2, 8, 8, 9] _nbins = 3 self._container = HistContainer(n_bins=_nbins, bin_range=(0.0, 10.), fill_data=_data) self._container.add_error(err_val=0.1, name='SC5A_1', correlation=0.5, relative=False) self._container.add_error( err_val=[i / 10. for i in range(1, _nbins + 1)], name='SC5A_2', correlation=0.5, relative=False) self._container.add_matrix_error(err_matrix=np.eye(_nbins) * 0.1, name='MCov', relative=False, matrix_type='covariance') self._container.add_matrix_error(err_matrix=np.eye(_nbins), name='MCor', relative=False, matrix_type='correlation', err_val=0.03) self._roundtrip_stringstream = IOStreamHandle(StringIO()) self._testfile_stringstream = IOStreamHandle( StringIO(TEST_DATASET_HIST)) self._roundtrip_streamreader = DataContainerYamlReader( self._roundtrip_stringstream) self._roundtrip_streamwriter = DataContainerYamlWriter( self._container, self._roundtrip_stringstream) self._testfile_streamreader = DataContainerYamlReader( self._testfile_stringstream) self._testfile_stringstream_missing_keyword = IOStreamHandle( StringIO(TEST_DATASET_HIST_MSSING_KEYWORD)) self._testfile_stringstream_extra_keyword = IOStreamHandle( StringIO(TEST_DATASET_HIST_EXTRA_KEYWORD)) self._testfile_streamreader_missing_keyword = DataContainerYamlReader( self._testfile_stringstream_missing_keyword) self._testfile_streamreader_extra_keyword = DataContainerYamlReader( self._testfile_stringstream_extra_keyword) self._ref_testfile_n_bins = 2 self._ref_testfile_bin_range = [1.0, 3.0] self._ref_testfile_bin_edges = [1.0, 2.0, 3.0] self._ref_testfile_data = [1, 1] self._ref_testfile_err = [0.89442719, 1] self._ref_testfile_cov_mat = np.array([[0.8, 0.3], [0.3, 1.0]]) self._ref_testfile_error_names = { 'ErrorOne', 'ErrorTwo', 'ErrorThree', 'ErrorFour' }
def setUp(self): _bin_edges = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0] _data = [ 0.5, 1.5, 1.5, 2.5, 2.5, 2.5, 3.5, 3.5, 3.5, 3.5, 4.5, 4.5, 4.5, 4.5, 4.5 ] self._means = np.array([3.654, 7.789]) self._vars = np.array([2.467, 1.543]) self._cov_mat_uncor = np.array([[self._vars[0], 0.0], [0.0, self._vars[1]]]) self._cov_mat_uncor_inv = np.linalg.inv(self._cov_mat_uncor) self._cov_mat_cor = np.array([[self._vars[0], 0.1], [0.1, self._vars[1]]]) self._cov_mat_cor_inv = np.linalg.inv(self._cov_mat_cor) self._cov_mat_simple_a_inv = np.array([[1.0 / self._vars[0], 0.0], [0.0, 0.0]]) self._cov_mat_simple_b_inv = np.array([[0.0, 0.0], [0.0, 1.0 / self._vars[1]]]) self._data_container = HistContainer(n_bins=5, bin_range=(0.0, 5.0), fill_data=_data, dtype=float) self._data_container.add_error(err_val=1.0) _a_test = np.linspace(start=1, stop=2, num=9, endpoint=True) _b_test = np.linspace(start=2, stop=3, num=9, endpoint=True) self._test_par_values = np.zeros((4, 2, 9)) self._test_par_values[0, 0] = _a_test self._test_par_values[1, 1] = _b_test self._test_par_values[2, 0] = _a_test self._test_par_values[2, 1] = _b_test self._test_par_values[3, 0] = _a_test self._test_par_values[3, 1] = _b_test[::-1] # reverse order self._test_par_res = self._test_par_values - self._means.reshape( (1, 2, 1)) self._test_par_res = np.transpose(self._test_par_res, axes=(0, 2, 1)) self._fit_no_constraints = HistFit( self._data_container, model_density_function=self._model_function, bin_evaluation=self._model_function_antiderivative) self._fit_no_constraints.do_fit() _cost_function = self._fit_no_constraints._fitter._fcn_wrapper self._profile_no_constraints = np.zeros((4, 9)) for _i in range(4): for _j in range(9): self._profile_no_constraints[_i, _j] = _cost_function( self._test_par_values[_i, 0, _j], self._test_par_values[_i, 1, _j])
def test_update_data(self): _fit = self._get_fit(model_density_antiderivative=hist_model_density_antideriv) _new_entries = np.array([ 19.424357258680693, 19.759361803397155, 18.364336396273362, 18.36464562195573, 21.12001399388072, 18.08232152646234, 20.881797998050466, 19.799071480564336, 20.00149234521563, 18.45879580610016, 20.507568873033875, 20.30648149729429, 20.877350386469818, 19.381958240681676, 19.64442840120083, 19.141355147035597, 19.94101630498644, 20.504982848860507, 17.524033521076785, 20.73543156774036, 21.163113940992737, 19.05979241568689, 20.196952015154917, 20.40130402635463, 21.80417387186999, 20.611530513961164, 19.099481246155072, 21.817653009909904, 19.75055842274471, 20.103815812928232, 18.174017677733538, 21.047249981725685, 21.262823911834488, 21.536864685525888, 18.813610758324447, 21.499655806695163, 19.933264932657465, 21.954933746841995, 17.78470283680212, 19.8917212343489, 19.372012624773838, 18.9520723656944, 19.9905553737993, 18.22737716365809, 22.208437406472243, 19.875706306083835, 19.17672889225326, 20.10750939196147, 20.093938177045032, 19.857292210131092, 20.17843836897936, 20.58803422718744, 19.936410829343984, 19.050688989087522, 18.46936492682146, 21.90955106395087, 19.661176212242154, 22.2764766192496, 19.850200163818528, 18.49289303805954, 19.7563960302135, 20.940311019530235, 19.12732791777932, 22.09224225083453, 20.225667564052465, 20.10787811564912, 18.660130651239726, 18.356069221596094, 20.278651217320608, 18.62176541545302, 18.747451690981315, 19.81307693501857, 19.34065619310232, 19.56998674371285, 19.885577923257177, 18.81752399043877, 20.67686318083984, 20.265021790145465, 19.982547007042093, 19.581967230877964, 18.486722000426457, 19.83143305661045, 21.252124382516378, 20.152988937293436, 18.917354464336892, 18.349803731030892, 21.32702043081207, 22.410955706069743, 20.972404800516973, 19.615870251101295, 19.013627387925588, 19.54487437668081, 20.538542465210206, 18.626198427902466, 20.221745437611307, 19.064952809088076]) _fit.data = HistContainer( 8, (17, 23), fill_data=_new_entries ) _fit.add_error(err_val=1.0) _ref_data, _ = np.histogram( _new_entries, bins=8, range=(17, 23) ) self._assert_fit_properties( _fit, dict( data=_ref_data, ) ) _fit.do_fit() self._assert_fit_properties( _fit, dict( data=_ref_data, parameter_values=np.array([19.83815938, 1.1729322]), poi_values=np.array([19.83815938, 1.1729322]), ), rtol=1e-2 )
def setUp(self): self._ref_entries = [-9999., -8279., 3.3, 5.5, 2.2, 8.5, 10., 10.2, 10000., 1e7] self._ref_n_bins_auto = 25 self._ref_n_bins_manual = 10 self._ref_n_bin_range = (0., 10.) self._ref_bin_edges_manual_equalspacing = np.linspace(0, 10, self._ref_n_bins_manual + 1) # [0 , 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10] self._ref_data_manual_equalspacing = np.array([ 0, 0, 1, 1, 0, 1, 0, 0, 1, 0]) self._ref_bin_edges_manual_variablespacing = [0 , 2 , 3 , 3.1 , 3.2 , 3.3 , 3.4 , 7 , 8.5 , 9 , 10] self._ref_data_manual_variablespacing = np.array([ 0, 1, 0, 0, 0, 1, 1, 0, 1, 0]) self._ref_data_auto = np.array([0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]) # test rebinning functionality self._probe_bin_edges_variablespacing_withedges = [0, 2, 3, 3.1, 3.2, 3.3, 3.4, 7, 8.5, 9, 10] # OK self._probe_bin_edges_variablespacing_noedges = [ 2, 3, 3.1, 3.2, 3.3, 3.4, 7, 8.5, 9] # OK self._probe_bin_edges_variablespacing_wrongedges1 = [0, 2, 3, 3.1, 3.2, 3.3, 3.4, 7, 8.5, 9, 12.3] # fail self._probe_bin_edges_variablespacing_wrongedges2 = [-9, 2, 3, 3.1, 3.2, 3.3, 3.4, 7, 8.5, 9, 10] # fail self._probe_bin_edges_variablespacing_wrongedges3 = [-3, 2, 3, 3.1, 3.2, 3.3, 3.4, 7, 8.5, 9, 22] # fail self._probe_bin_edges_variablespacing_wrongnumber = [0, 2, 3, 3.1, 3.2, 3.3, 3.4, 7, 8.5, 10] # fail self._probe_bin_edges_variablespacing_unsorted = [0, 2, 3, 8.5, 3.2, 3.3, 3.4, 7, 3.1, 9, 10] # fail self.hist_cont_binedges_auto = HistContainer(self._ref_n_bins_auto, self._ref_n_bin_range, bin_edges=None) self.hist_cont_binedges_manual_equal = HistContainer(self._ref_n_bins_manual, self._ref_n_bin_range, bin_edges=self._ref_bin_edges_manual_equalspacing) self.hist_cont_binedges_manual_variable = HistContainer(self._ref_n_bins_manual, self._ref_n_bin_range, bin_edges=self._ref_bin_edges_manual_variablespacing)
def setUp(self): self._ref_n_bins = 11 self._ref_n_bin_range = (-3, 25) self._ref_bin_edges = np.linspace(self._ref_n_bin_range[0], self._ref_n_bin_range[1], self._ref_n_bins + 1) self._ref_entries = np.array([ 11.47195963, 9.96715403, 19.90275216, 13.65225802, 18.52670233, 17.79707059, 11.5441954, 18.42331074, 13.3808496, 18.40632518, 13.21694177, 15.34569261, 9.85164713, 11.56275047, 12.42687109, 9.43924719, 15.03632673, 10.76203991, 15.16788754, 13.78683534, 10.84686619, 17.29655143, 12.58404203, 13.10171453, 20.23773075, 15.00936796, 15.38972301, 12.94254624, 19.01920031, 19.67747312, 14.53196033, 13.78898329, 17.34040707, 17.25328064, 13.64052295, 11.80461841, 14.21872985, 11.59054406, 20.28243216, 15.43544627, 15.85964469, 13.68948162, 14.55574162, 15.70198922, 12.85548112, 14.05419407, 15.85172907, 19.2951937, 12.70652203, 13.47822913, 12.84301792, 13.65407699, 12.81908235, 12.73065184, 23.34917367, 11.38895625, 9.41615435, 7.24554349, 11.4112971, 14.54736404, 15.84186607, 15.33880379, 9.93177073, 15.14220967, 15.19535013, 11.00033758, 11.83671083, 13.02411982, 18.43323913, 12.06272124, 20.57549426, 12.97405382, 13.35969913, 10.76072424, 16.35912216, 14.52842827, 16.97221766, 13.02791932, 14.36097113, 16.93839498, 15.60091018, 11.55044489, 14.06915886, 17.64985576, 11.59865691, 13.41486068, 14.25999508, 10.70887047, 4.08280244, 13.1043861, 14.62321312, 14.85894591, 14.89235398, 10.60967181, 15.22310211, 10.77853626, 14.56823312, 14.46093346, 13.34031129, 14.14203599 ]) self._ref_hist_cont = HistContainer(self._ref_n_bins, self._ref_n_bin_range, bin_edges=None, fill_data=self._ref_entries) # reference initial values self._ref_initial_pars = np.array([14., 3.]) self._ref_initial_model = (hist_model_density_antideriv( self._ref_bin_edges[1:], * self._ref_initial_pars) - hist_model_density_antideriv( self._ref_bin_edges[:-1], *self._ref_initial_pars)) * len( self._ref_entries) # fit data self._ref_data, _ = np.histogram(self._ref_entries, bins=self._ref_n_bins, range=self._ref_n_bin_range) # pre-fit cost value self._ref_initial_cost_nll = \ -2*np.sum(stats.poisson.logpmf(self._ref_data, self._ref_initial_model)) self._ref_initial_cost_chi2 = simple_chi2(self._ref_data, self._ref_initial_model) # reference fit result values self._nominal_fit_result_pars_nll = np.array([14.18427759, 3.02257722]) self._nominal_fit_result_pars_chi2 = np.array( [13.82779489, 2.62746457]) self._nominal_fit_result_model_nll = (hist_model_density_antideriv( self._ref_bin_edges[1:], * self._nominal_fit_result_pars_nll) - hist_model_density_antideriv( self._ref_bin_edges[:-1], * self._nominal_fit_result_pars_nll)) * len(self._ref_entries) self._nominal_fit_result_model_chi2 = (hist_model_density_antideriv( self._ref_bin_edges[1:], * self._nominal_fit_result_pars_chi2) - hist_model_density_antideriv( self._ref_bin_edges[:-1], * self._nominal_fit_result_pars_chi2)) * len(self._ref_entries) self._nominal_fit_result_cost_nll = \ -2*np.sum(stats.poisson.logpmf(self._ref_data, self._nominal_fit_result_model_nll)) self._nominal_fit_result_cost_chi2 = simple_chi2( self._ref_data, self._nominal_fit_result_model_chi2) # helper dict with all reference property values self._ref_prop_dict = dict( did_fit=False, model_count=1, parameter_values=self._ref_initial_pars, parameter_names=('mu', 'sigma'), data=self._ref_data, model=self._ref_initial_model, )
class TestParameterConstraintInHistFit(unittest.TestCase): @staticmethod def _model_function(x, a, b): return a * x + b @staticmethod def _model_function_antiderivative(x, a, b): return 0.5 * a * x ** 2 + b * x def _expected_profile_diff(self, res, cov_mat_inv): return res.dot(cov_mat_inv).dot(res) def _test_consistency(self, constrained_fit, par_cov_mat_inv): constrained_fit.do_fit() _cost_function = constrained_fit._fitter._fcn_wrapper for _i in range(4): for _j in range(9): _a = self._test_par_values[_i, 0, _j] _b = self._test_par_values[_i, 1, _j] _profile_constrained = _cost_function(self._test_par_values[_i, 0, _j], self._test_par_values[_i, 1, _j]) _diff = _profile_constrained - self._profile_no_constraints[_i, _j] _expected_profile_diff = self._expected_profile_diff(self._test_par_res[_i, _j], par_cov_mat_inv) self.assertTrue(np.abs(_diff - _expected_profile_diff) < 1e-12) def setUp(self): _bin_edges = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0] _data = [ 0.5, 1.5, 1.5, 2.5, 2.5, 2.5, 3.5, 3.5, 3.5, 3.5, 4.5, 4.5, 4.5, 4.5, 4.5 ] self._means = np.array([3.654, 7.789]) self._vars = np.array([2.467, 1.543]) self._cov_mat_uncor = np.array([[self._vars[0], 0.0], [0.0, self._vars[1]]]) self._cov_mat_uncor_inv = np.linalg.inv(self._cov_mat_uncor) self._cov_mat_cor = np.array([[self._vars[0], 0.1], [0.1, self._vars[1]]]) self._cov_mat_cor_inv = np.linalg.inv(self._cov_mat_cor) self._cov_mat_simple_a_inv = np.array([[1.0 / self._vars[0], 0.0], [0.0, 0.0]]) self._cov_mat_simple_b_inv = np.array([[0.0, 0.0], [0.0, 1.0 / self._vars[1]]]) self._data_container = HistContainer(n_bins=5, bin_range=(0.0, 5.0), fill_data=_data, dtype=float) self._data_container.add_error(err_val=1.0) _a_test = np.linspace(start=1, stop=2, num=9, endpoint=True) _b_test = np.linspace(start=2, stop=3, num=9, endpoint=True) self._test_par_values = np.zeros((4, 2, 9)) self._test_par_values[0, 0] = _a_test self._test_par_values[1, 1] = _b_test self._test_par_values[2, 0] = _a_test self._test_par_values[2, 1] = _b_test self._test_par_values[3, 0] = _a_test self._test_par_values[3, 1] = _b_test[::-1] # reverse order self._test_par_res = self._test_par_values - self._means.reshape((1, 2, 1)) self._test_par_res = np.transpose(self._test_par_res, axes=(0, 2, 1)) self._fit_no_constraints = HistFit(self._data_container, model_density_function=self._model_function, model_density_antiderivative=self._model_function_antiderivative) self._fit_no_constraints.do_fit() _cost_function = self._fit_no_constraints._fitter._fcn_wrapper self._profile_no_constraints = np.zeros((4, 9)) for _i in range(4): for _j in range(9): self._profile_no_constraints[_i, _j] = _cost_function( self._test_par_values[_i, 0, _j], self._test_par_values[_i, 1, _j]) def test_bad_input_exception(self): _fit_with_constraint = HistFit(self._data_container, model_density_function=self._model_function, model_density_antiderivative=self._model_function_antiderivative) with self.assertRaises(HistFitException): _fit_with_constraint.add_parameter_constraint('c', 1.0, 1.0) with self.assertRaises(HistFitException): _fit_with_constraint.add_matrix_parameter_constraint(['a', 'c'], [1.0, 2.0], [[0.2, 0.0], [0.0, 0.1]]) with self.assertRaises(HistFitException): _fit_with_constraint.add_matrix_parameter_constraint(['a'], [1.0, 2.0], [[0.2, 0.0], [0.0, 0.1]]) def test_fit_profile_cov_mat_uncorrelated(self): _fit_with_constraint = HistFit(self._data_container, model_density_function=self._model_function, model_density_antiderivative=self._model_function_antiderivative) _fit_with_constraint.add_matrix_parameter_constraint(['a', 'b'], self._means, self._cov_mat_uncor) self._test_consistency(_fit_with_constraint, self._cov_mat_uncor_inv) _fit_with_constraint_alt = HistFit(self._data_container, model_density_function=self._model_function, model_density_antiderivative=self._model_function_antiderivative) _fit_with_constraint_alt.add_parameter_constraint('a', self._means[0], np.sqrt(self._vars[0])) _fit_with_constraint_alt.add_parameter_constraint('b', self._means[1], np.sqrt(self._vars[1])) self._test_consistency(_fit_with_constraint_alt, self._cov_mat_uncor_inv) def test_fit_profile_cov_mat_correlated(self): _fit_with_constraint = HistFit(self._data_container, model_density_function=self._model_function, model_density_antiderivative=self._model_function_antiderivative) _fit_with_constraint.add_matrix_parameter_constraint(['a', 'b'], self._means, self._cov_mat_cor) self._test_consistency(_fit_with_constraint, self._cov_mat_cor_inv) def test_fit_profile_simple_a(self): _fit_with_constraint = HistFit(self._data_container, model_density_function=self._model_function, model_density_antiderivative=self._model_function_antiderivative) _fit_with_constraint.add_parameter_constraint('a', self._means[0], np.sqrt(self._vars[0])) self._test_consistency(_fit_with_constraint, self._cov_mat_simple_a_inv) def test_fit_profile_simple_b(self): _fit_with_constraint = HistFit(self._data_container, model_density_function=self._model_function, model_density_antiderivative=self._model_function_antiderivative) _fit_with_constraint.add_parameter_constraint('b', self._means[1], np.sqrt(self._vars[1])) self._test_consistency(_fit_with_constraint, self._cov_mat_simple_b_inv)
def test_raise_construct_bin_edges_variablespacing_unsorted(self): with self.assertRaises(HistContainerException): _hc = HistContainer( self._ref_n_bins_manual, self._ref_n_bin_range, bin_edges=self._probe_bin_edges_variablespacing_unsorted)
def test_construct_bin_edges_variablespacing_noedges(self): _hc = HistContainer( self._ref_n_bins_manual, self._ref_n_bin_range, bin_edges=self._probe_bin_edges_variablespacing_noedges)
class TestDatastoreHistogram(unittest.TestCase): def setUp(self): self._ref_entries = [ -9999., -8279., 3.3, 5.5, 2.2, 8.5, 10., 10.2, 10000., 1e7 ] self._ref_n_bins_auto = 25 self._ref_n_bins_manual = 10 self._ref_n_bin_range = (0., 10.) self._ref_bin_edges_manual_equalspacing = np.linspace( 0, 10, self._ref_n_bins_manual + 1) # [0 , 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10] self._ref_data_manual_equalspacing = np.array( [0, 0, 1, 1, 0, 1, 0, 0, 1, 0]) self._ref_bin_heights_manual = np.array( [5, 4, 10, 3, 0, 15, 7, 4, 20, 1]) self._ref_bin_edges_manual_variablespacing = [ 0, 2, 3, 3.1, 3.2, 3.3, 3.4, 7, 8.5, 9, 10 ] self._ref_data_manual_variablespacing = np.array( [0, 1, 0, 0, 0, 1, 1, 0, 1, 0]) self._ref_data_auto = np.array([ 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0 ]) # test rebinning functionality self._probe_bin_edges_variablespacing_withedges = [ 0, 2, 3, 3.1, 3.2, 3.3, 3.4, 7, 8.5, 9, 10 ] # OK self._probe_bin_edges_variablespacing_noedges = [ 2, 3, 3.1, 3.2, 3.3, 3.4, 7, 8.5, 9 ] # OK self._probe_bin_edges_variablespacing_wrongedges1 = [ 0, 2, 3, 3.1, 3.2, 3.3, 3.4, 7, 8.5, 9, 12.3 ] # fail self._probe_bin_edges_variablespacing_wrongedges2 = [ -9, 2, 3, 3.1, 3.2, 3.3, 3.4, 7, 8.5, 9, 10 ] # fail self._probe_bin_edges_variablespacing_wrongedges3 = [ -3, 2, 3, 3.1, 3.2, 3.3, 3.4, 7, 8.5, 9, 22 ] # fail self._probe_bin_edges_variablespacing_wrongnumber = [ 0, 2, 3, 3.1, 3.2, 3.3, 3.4, 7, 8.5, 10 ] # fail self._probe_bin_edges_variablespacing_unsorted = [ 0, 2, 3, 8.5, 3.2, 3.3, 3.4, 7, 3.1, 9, 10 ] # fail self.hist_cont_binedges_auto = HistContainer(self._ref_n_bins_auto, self._ref_n_bin_range, bin_edges=None) self.hist_cont_binedges_manual_equal = HistContainer( self._ref_n_bins_manual, self._ref_n_bin_range, bin_edges=self._ref_bin_edges_manual_equalspacing) self.hist_cont_binedges_manual_variable = HistContainer( self._ref_n_bins_manual, self._ref_n_bin_range, bin_edges=self._ref_bin_edges_manual_variablespacing) def test_property_size(self): self.assertEqual(self.hist_cont_binedges_auto.size, self._ref_n_bins_auto) self.assertEqual(self.hist_cont_binedges_manual_equal.size, self._ref_n_bins_manual) self.assertEqual(self.hist_cont_binedges_manual_variable.size, self._ref_n_bins_manual) def test_property_low(self): self.assertEqual(self.hist_cont_binedges_auto.low, self._ref_n_bin_range[0]) self.assertEqual(self.hist_cont_binedges_manual_equal.low, self._ref_n_bin_range[0]) self.assertEqual(self.hist_cont_binedges_manual_variable.low, self._ref_n_bin_range[0]) def test_property_high(self): self.assertEqual(self.hist_cont_binedges_auto.high, self._ref_n_bin_range[1]) self.assertEqual(self.hist_cont_binedges_manual_equal.high, self._ref_n_bin_range[1]) self.assertEqual(self.hist_cont_binedges_manual_variable.high, self._ref_n_bin_range[1]) def test_fill_empty_binedges_auto_compare_data(self): self.hist_cont_binedges_auto.fill(self._ref_entries) self.assertTrue( np.allclose(self.hist_cont_binedges_auto.data, self._ref_data_auto)) def test_fill_empty_binedges_manual_equal_compare_data(self): self.hist_cont_binedges_manual_equal.fill(self._ref_entries) self.assertTrue( np.allclose(self.hist_cont_binedges_manual_equal.data, self._ref_data_manual_equalspacing)) def test_fill_empty_binedges_manual_variable_compare_data(self): self.hist_cont_binedges_manual_variable.fill(self._ref_entries) self.assertTrue( np.allclose(self.hist_cont_binedges_manual_variable.data, self._ref_data_manual_variablespacing)) def test_fill_empty_binedges_auto_rebin_manual_equal_compare_data(self): self.hist_cont_binedges_auto.fill(self._ref_entries) self.hist_cont_binedges_auto.rebin( self._ref_bin_edges_manual_equalspacing) self.assertTrue( np.allclose(self.hist_cont_binedges_auto.data, self._ref_data_manual_equalspacing)) def test_fill_empty_binedges_auto_rebin_manual_variable_compare_data(self): self.hist_cont_binedges_auto.fill(self._ref_entries) self.hist_cont_binedges_auto.rebin( self._ref_bin_edges_manual_variablespacing) self.assertTrue( np.allclose(self.hist_cont_binedges_auto.data, self._ref_data_manual_variablespacing)) def test_manual_bin_height(self): self.hist_cont_binedges_manual_equal.set_bins( self._ref_bin_heights_manual) self.assertTrue( np.alltrue(self.hist_cont_binedges_manual_equal.data == self._ref_bin_heights_manual)) self.assertTrue(self.hist_cont_binedges_manual_equal._manual_heights) def test_construct_bin_edges_variablespacing_withedges(self): _hc = HistContainer( self._ref_n_bins_manual, self._ref_n_bin_range, bin_edges=self._probe_bin_edges_variablespacing_withedges) def test_construct_bin_edges_variablespacing_noedges(self): _hc = HistContainer( self._ref_n_bins_manual, self._ref_n_bin_range, bin_edges=self._probe_bin_edges_variablespacing_noedges) def test_raise_construct_bin_edges_variablespacing_wrongedges1(self): with self.assertRaises(HistContainerException): _hc = HistContainer( self._ref_n_bins_manual, self._ref_n_bin_range, bin_edges=self._probe_bin_edges_variablespacing_wrongedges1) def test_raise_construct_bin_edges_variablespacing_wrongedges2(self): with self.assertRaises(HistContainerException): _hc = HistContainer( self._ref_n_bins_manual, self._ref_n_bin_range, bin_edges=self._probe_bin_edges_variablespacing_wrongedges2) def test_raise_construct_bin_edges_variablespacing_wrongedges3(self): with self.assertRaises(HistContainerException): _hc = HistContainer( self._ref_n_bins_manual, self._ref_n_bin_range, bin_edges=self._probe_bin_edges_variablespacing_wrongedges3) def test_raise_construct_bin_edges_variablespacing_wrongnumber(self): with self.assertRaises(HistContainerException): _hc = HistContainer( self._ref_n_bins_manual, self._ref_n_bin_range, bin_edges=self._probe_bin_edges_variablespacing_wrongnumber) def test_raise_construct_bin_edges_variablespacing_unsorted(self): with self.assertRaises(HistContainerException): _hc = HistContainer( self._ref_n_bins_manual, self._ref_n_bin_range, bin_edges=self._probe_bin_edges_variablespacing_unsorted) def test_raise_add_same_error_name_twice(self): self.hist_cont_binedges_auto.add_error(0.1, name="MyNewError", correlation=0, relative=False) with self.assertRaises(DataContainerException): self.hist_cont_binedges_auto.add_error(0.1, name="MyNewError", correlation=0, relative=False) def test_raise_get_inexistent_error(self): with self.assertRaises(DataContainerException): self.hist_cont_binedges_auto.get_error("MyInexistentError") def test_raise_manual_bin_heights_rebin(self): self.hist_cont_binedges_manual_equal.set_bins( self._ref_bin_heights_manual) with self.assertRaises(DataContainerException): self.hist_cont_binedges_manual_equal.rebin( self._ref_bin_edges_manual_variablespacing)
class TestHistContainerYamlRepresentation(unittest.TestCase): def setUp(self): _data = [1, 1, 4, 1, 1, 4, 2, 5, 5, 5, 2, 4, 2, 2, 18, 2, 8, 8, 9] _nbins = 3 self._container = HistContainer(n_bins=_nbins, bin_range=(0.0, 10.), fill_data=_data) self._container.add_error(err_val=0.1, name='SC5A_1', correlation=0.5, relative=False) self._container.add_error( err_val=[i / 10. for i in range(1, _nbins + 1)], name='SC5A_2', correlation=0.5, relative=False) self._container.add_matrix_error(err_matrix=np.eye(_nbins) * 0.1, name='MCov', relative=False, matrix_type='covariance') self._container.add_matrix_error(err_matrix=np.eye(_nbins), name='MCor', relative=False, matrix_type='correlation', err_val=0.03) self._roundtrip_stringstream = IOStreamHandle(StringIO()) self._testfile_stringstream = IOStreamHandle( StringIO(TEST_DATASET_HIST)) self._roundtrip_streamreader = DataContainerYamlReader( self._roundtrip_stringstream) self._roundtrip_streamwriter = DataContainerYamlWriter( self._container, self._roundtrip_stringstream) self._testfile_streamreader = DataContainerYamlReader( self._testfile_stringstream) self._testfile_stringstream_missing_keyword = IOStreamHandle( StringIO(TEST_DATASET_HIST_MSSING_KEYWORD)) self._testfile_stringstream_extra_keyword = IOStreamHandle( StringIO(TEST_DATASET_HIST_EXTRA_KEYWORD)) self._testfile_streamreader_missing_keyword = DataContainerYamlReader( self._testfile_stringstream_missing_keyword) self._testfile_streamreader_extra_keyword = DataContainerYamlReader( self._testfile_stringstream_extra_keyword) self._ref_testfile_n_bins = 2 self._ref_testfile_bin_range = [1.0, 3.0] self._ref_testfile_bin_edges = [1.0, 2.0, 3.0] self._ref_testfile_data = [1, 1] self._ref_testfile_err = [0.89442719, 1] self._ref_testfile_cov_mat = np.array([[0.8, 0.3], [0.3, 1.0]]) self._ref_testfile_error_names = { 'ErrorOne', 'ErrorTwo', 'ErrorThree', 'ErrorFour' } def test_write_to_roundtrip_stringstream(self): self._roundtrip_streamwriter.write() def test_read_from_testfile_stream(self): _read_container = self._testfile_streamreader.read() self.assertTrue(isinstance(_read_container, HistContainer)) self.assertTrue(_read_container.n_bins == self._ref_testfile_n_bins) self.assertTrue( np.allclose(_read_container.bin_range, self._ref_testfile_bin_range)) self.assertTrue( np.allclose(_read_container.bin_edges, self._ref_testfile_bin_edges)) self.assertTrue( np.allclose(_read_container.data, self._ref_testfile_data)) self.assertTrue( np.allclose(_read_container.err, self._ref_testfile_err)) self.assertTrue( np.allclose( _read_container.cov_mat, self._ref_testfile_cov_mat, )) # check that the error names are the same self.assertEqual(set(_read_container._error_dicts.keys()), self._ref_testfile_error_names) def test_read_from_testfile_stream_missing_keyword(self): with self.assertRaises(YamlReaderException): self._testfile_streamreader_missing_keyword.read() def test_read_from_testfile_stream_extra_keyword(self): with self.assertRaises(YamlReaderException): self._testfile_streamreader_extra_keyword.read() def test_round_trip_with_stringstream(self): self._roundtrip_streamwriter.write() self._roundtrip_stringstream.seek(0) # return to beginning _read_container = self._roundtrip_streamreader.read() self.assertTrue(isinstance(_read_container, HistContainer)) self.assertTrue(self._container.n_bins == _read_container.n_bins) self.assertTrue( np.allclose(self._container.bin_range, _read_container.bin_range)) self.assertTrue( np.allclose(self._container.bin_edges, _read_container.bin_edges)) # compare data members self.assertTrue(np.allclose(self._container.data, _read_container.data)) # compare (total) errors and cov mats # TODO: compare individual error components -> nontrivial because error ids change... self.assertTrue(np.allclose(self._container.err, _read_container.err)) self.assertTrue( np.allclose(self._container.cov_mat, _read_container.cov_mat)) # check that the error names are the same self.assertEqual(set(self._container._error_dicts.keys()), set(_read_container._error_dicts.keys()))