def test_fit_single_spectrum_tof_including_background(self):
flags = self._create_test_flags(background=True)
runs = "15039-15045"
fit_results = fit_tof(runs, flags)
self.assertTrue(isinstance(fit_results, tuple))
self.assertEqual(4, len(fit_results))
fitted_wsg = fit_results[0]
self.assertTrue(isinstance(fitted_wsg, WorkspaceGroup))
self.assertEqual(2, len(fitted_wsg))
fitted_ws = fitted_wsg[0]
self.assertTrue(isinstance(fitted_ws, MatrixWorkspace))
self.assertEqual(8, fitted_ws.getNumberHistograms())
# self.assertAlmostEqual(50.0, fitted_ws.readX(0)[0])
# self.assertAlmostEqual(562.0, fitted_ws.readX(0)[-1])
# self.assertAlmostEqual(0.000928695463881635, fitted_ws.readY(0)[0])
# self.assertAlmostEqual(0.00722948549525415, fitted_ws.readY(0)[-1])
# self.assertAlmostEqual(-0.00756178413274695, fitted_ws.readY(1)[0])
# self.assertAlmostEqual(0.00355843687365601, fitted_ws.readY(1)[-1])
fitted_params = fit_results[1]
self.assertTrue(isinstance(fitted_params, MatrixWorkspace))
self.assertEqual(14, fitted_params.getNumberHistograms())
chisq_values = fit_results[2]
self.assertTrue(isinstance(chisq_values, list))
self.assertEqual(1, len(chisq_values))
exit_iteration = fit_results[3]
self.assertTrue(isinstance(exit_iteration, int))
def test_fit_single_spectrum_tof_and_no_background(self):
flags = self._create_test_flags(background=False)
runs = "15039-15045"
fit_results = fit_tof(runs, flags)
self.assertTrue(isinstance(fit_results, tuple))
self.assertEqual(4, len(fit_results))
fitted_wsg = fit_results[0]
self.assertTrue(isinstance(fitted_wsg, WorkspaceGroup))
self.assertEqual(2, len(fitted_wsg))
fitted_ws = fitted_wsg[0]
self.assertTrue(isinstance(fitted_ws, MatrixWorkspace))
self.assertEqual(7, fitted_ws.getNumberHistograms())
# self.assertAlmostEqual(50.0, fitted_ws.readX(0)[0])
# self.assertAlmostEqual(562.0, fitted_ws.readX(0)[-1])
# self.assertAlmostEqual(0.000928695463881635, fitted_ws.readY(0)[0])
# self.assertAlmostEqual(0.00722948549525415, fitted_ws.readY(0)[-1])
# self.assertAlmostEqual(1.45746507977816e-05, fitted_ws.readY(1)[0])
# self.assertAlmostEqual(7.33791942084561e-05, fitted_ws.readY(1)[-1])
fitted_params = fit_results[1]
self.assertTrue(isinstance(fitted_params, MatrixWorkspace))
self.assertEqual(10, fitted_params.getNumberHistograms())
chisq_values = fit_results[2]
self.assertTrue(isinstance(chisq_values, list))
self.assertEqual(1, len(chisq_values))
exit_iteration = fit_results[3]
self.assertTrue(isinstance(exit_iteration, int))
def test_fit_bank_by_bank_for_forward_spectra_tof_and_no_background(self):
flags = self._create_test_flags(background=False)
flags['fit_mode'] = 'bank'
flags['spectra'] = 'forward'
runs = "15039-15045"
fit_results = fit_tof(runs, flags)
self.assertTrue(isinstance(fit_results, tuple))
self.assertEquals(4, len(fit_results))
fitted_banks = fit_results[0]
self.assertTrue(isinstance(fitted_banks, list))
self.assertEqual(8, len(fitted_banks))
bank1 = fitted_banks[0]
self.assertTrue(isinstance(bank1, WorkspaceGroup))
bank1_data = bank1[0]
self.assertTrue(isinstance(bank1_data, MatrixWorkspace))
# self.assertAlmostEqual(50.0, bank1_data.readX(0)[0])
# self.assertAlmostEqual(562.0, bank1_data.readX(0)[-1])
# self.assertAlmostEqual(0.000107272755986595, bank1_data.readY(1)[0])
# self.assertAlmostEqual(0.000585633970072128, bank1_data.readY(1)[-1])
bank8 = fitted_banks[-1]
self.assertTrue(isinstance(bank8, WorkspaceGroup))
bank8_data = bank8[0]
self.assertTrue(isinstance(bank8_data, MatrixWorkspace))
# self.assertAlmostEqual(50.0, bank8_data.readX(0)[0])
# self.assertAlmostEqual(562.0, bank8_data.readX(0)[-1])
# self.assertAlmostEqual(0.000596850729120898, bank8_data.readY(1)[0])
# self.assertAlmostEqual(0.000529343513813141, bank8_data.readY(1)[-1])
chisq_values = fit_results[2]
self.assertTrue(isinstance(chisq_values, list))
self.assertEqual(8, len(chisq_values))
exit_iteration = fit_results[3]
self.assertTrue(isinstance(exit_iteration, int))
def test_fit_spectra_by_spectra_for_forward_spectra_tof_and_no_background(self):
flags = self._create_test_flags(background=False)
flags['fit_mode'] = 'spectra'
flags['spectra'] = '143-144'
runs = "15039-15045"
fit_results = fit_tof(runs, flags)
self.assertTrue(isinstance(fit_results, tuple))
self.assertEquals(4, len(fit_results))
fitted_spec = fit_results[0]
self.assertTrue(isinstance(fitted_spec, list))
self.assertEqual(2, len(fitted_spec))
spec143 = fitted_spec[0]
self.assertTrue(isinstance(spec143, WorkspaceGroup))
spec143_data = spec143[0]
self.assertTrue(isinstance(spec143_data, MatrixWorkspace))
# self.assertAlmostEqual(50.0, spec143_data.readX(0)[0])
# self.assertAlmostEqual(562.0, spec143_data.readX(0)[-1])
# self.assertAlmostEqual(2.37897941103748e-06, spec143_data.readY(1)[0])
# self.assertAlmostEqual(3.58226563303213e-05, spec143_data.readY(1)[-1])
spec144 = fitted_spec[-1]
self.assertTrue(isinstance(spec144, WorkspaceGroup))
spec144_data = spec144[0]
self.assertTrue(isinstance(spec144_data, MatrixWorkspace))
# self.assertAlmostEqual(50.0, spec144_data.readX(0)[0])
# self.assertAlmostEqual(562.0, spec144_data.readX(0)[-1])
# self.assertAlmostEqual(5.57952304659615e-06, spec144_data.readY(1)[0])
# self.assertAlmostEqual(6.00056973529846e-05, spec144_data.readY(1)[-1])
chisq_values = fit_results[2]
self.assertTrue(isinstance(chisq_values, list))
self.assertEqual(2, len(chisq_values))
exit_iteration = fit_results[3]
self.assertTrue(isinstance(exit_iteration, int))
flags['gamma_correct'] = True flags['fixed_gamma_scaling'] = None flags['ms_flags'] = dict() flags['ms_flags']['SampleWidth'] = 10.0 flags['ms_flags']['SampleHeight'] = 10.0 flags['ms_flags']['SampleDepth'] = 0.5 flags['ms_flags']['SampleDensity'] = 241 # Optional parameters (default values are given) # flags['ms_flags']['Seed'] = 123456789 # flags['ms_flags']['NumScatters'] = 3 # flags['ms_flags']['NumRuns'] = 10 # flags['ms_flags']['NumEvents'] = 50000 # flags['ms_flags']['SmoothNeighbours'] = 3 # flags['ms_flags']['BeamRadius'] = 2.5 ## Advanced flags flags['ip_file'] = 'IP0004_10.par' flags['diff_mode'] = 'single' flags['max_fit_iterations'] = 5000 flags['fit_minimizer'] = 'Levenberg-Marquardt,AbsError=1e-08,RelError=1e-08' flags['iterations'] = 1 flags['convergence'] = None ## Run fit fit_tof(runs, flags, flags['iterations'], flags['convergence'])
flags['gamma_correct'] = True flags['fixed_gamma_scaling'] = None flags['ms_flags'] = dict() flags['ms_flags']['SampleWidth'] = 10.0 flags['ms_flags']['SampleHeight'] = 10.0 flags['ms_flags']['SampleDepth'] = 0.5 flags['ms_flags']['SampleDensity'] = 241 # Optional parameters (default values are given) # flags['ms_flags']['Seed'] = 123456789 # flags['ms_flags']['NumScatters'] = 3 # flags['ms_flags']['NumRuns'] = 10 # flags['ms_flags']['NumEvents'] = 50000 # flags['ms_flags']['SmoothNeighbours'] = 3 # flags['ms_flags']['BeamRadius'] = 2.5 ## Advanced flags flags['ip_file'] = 'IP0004_10.par' flags['diff_mode'] = 'single' flags['max_fit_iterations'] = 5000 flags['fit_minimizer'] = 'Levenberg-Marquardt,AbsError=1e-08,RelError=1e-08' flags['iterations'] = 1 flags['convergence'] = None ## Run fit fit_tof(runs, flags, flags['iterations'], flags['convergence'])
