diff options
Diffstat (limited to 'silx/math/fit/test/test_fitmanager.py')
| -rw-r--r-- | silx/math/fit/test/test_fitmanager.py | 87 |
1 files changed, 51 insertions, 36 deletions
diff --git a/silx/math/fit/test/test_fitmanager.py b/silx/math/fit/test/test_fitmanager.py index 38c4802..7a643cb 100644 --- a/silx/math/fit/test/test_fitmanager.py +++ b/silx/math/fit/test/test_fitmanager.py @@ -1,6 +1,6 @@ # coding: utf-8 # /*########################################################################## -# Copyright (C) 2016-2017 European Synchrotron Radiation Facility +# Copyright (C) 2016-2020 European Synchrotron Radiation Facility # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal @@ -35,6 +35,7 @@ from silx.math.fit import bgtheories from silx.math.fit.fittheory import FitTheory from silx.math.fit.functions import sum_gauss, sum_stepdown, sum_stepup +from silx.utils.testutils import ParametricTestCase from silx.test.utils import temp_dir custom_function_definition = """ @@ -110,7 +111,7 @@ def _order_of_magnitude(x): return numpy.log10(x).round() -class TestFitmanager(unittest.TestCase): +class TestFitmanager(ParametricTestCase): """ Unit tests of multi-peak functions. """ @@ -132,40 +133,54 @@ class TestFitmanager(unittest.TestCase): linear_bg = 2.65 * x + 13 y = linear_bg + sum_gauss(x, *p) - # Fitting - fit = fitmanager.FitManager() - fit.setdata(x=x, y=y) - fit.loadtheories(fittheories) - # Use one of the default fit functions - fit.settheory('Gaussians') - fit.setbackground('Linear') - fit.estimate() - fit.runfit() - - # fit.fit_results[] - - # first 2 parameters are related to the linear background - self.assertEqual(fit.fit_results[0]["name"], "Constant") - self.assertAlmostEqual(fit.fit_results[0]["fitresult"], 13) - self.assertEqual(fit.fit_results[1]["name"], "Slope") - self.assertAlmostEqual(fit.fit_results[1]["fitresult"], 2.65) - - for i, param in enumerate(fit.fit_results[2:]): - param_number = i // 3 + 1 - if i % 3 == 0: - self.assertEqual(param["name"], - "Height%d" % param_number) - elif i % 3 == 1: - self.assertEqual(param["name"], - "Position%d" % param_number) - elif i % 3 == 2: - self.assertEqual(param["name"], - "FWHM%d" % param_number) - - self.assertAlmostEqual(param["fitresult"], - p[i]) - self.assertAlmostEqual(_order_of_magnitude(param["estimation"]), - _order_of_magnitude(p[i])) + y_with_nans = numpy.array(y) + y_with_nans[::10] = numpy.nan + + x_with_nans = numpy.array(x) + x_with_nans[5::15] = numpy.nan + + tests = { + 'all finite': (x, y), + 'y with NaNs': (x, y_with_nans), + 'x with NaNs': (x_with_nans, y), + } + + for name, (xdata, ydata) in tests.items(): + with self.subTest(name=name): + # Fitting + fit = fitmanager.FitManager() + fit.setdata(x=xdata, y=ydata) + fit.loadtheories(fittheories) + # Use one of the default fit functions + fit.settheory('Gaussians') + fit.setbackground('Linear') + fit.estimate() + fit.runfit() + + # fit.fit_results[] + + # first 2 parameters are related to the linear background + self.assertEqual(fit.fit_results[0]["name"], "Constant") + self.assertAlmostEqual(fit.fit_results[0]["fitresult"], 13) + self.assertEqual(fit.fit_results[1]["name"], "Slope") + self.assertAlmostEqual(fit.fit_results[1]["fitresult"], 2.65) + + for i, param in enumerate(fit.fit_results[2:]): + param_number = i // 3 + 1 + if i % 3 == 0: + self.assertEqual(param["name"], + "Height%d" % param_number) + elif i % 3 == 1: + self.assertEqual(param["name"], + "Position%d" % param_number) + elif i % 3 == 2: + self.assertEqual(param["name"], + "FWHM%d" % param_number) + + self.assertAlmostEqual(param["fitresult"], + p[i]) + self.assertAlmostEqual(_order_of_magnitude(param["estimation"]), + _order_of_magnitude(p[i])) def testLoadCustomFitFunction(self): """Test FitManager using a custom fit function defined in an external |