diff options
Diffstat (limited to 'silx/math/fit/test/test_bgtheories.py')
| -rw-r--r-- | silx/math/fit/test/test_bgtheories.py | 169 |
1 files changed, 0 insertions, 169 deletions
diff --git a/silx/math/fit/test/test_bgtheories.py b/silx/math/fit/test/test_bgtheories.py deleted file mode 100644 index e9fea37..0000000 --- a/silx/math/fit/test/test_bgtheories.py +++ /dev/null @@ -1,169 +0,0 @@ -# coding: utf-8 -# /*########################################################################## -# Copyright (C) 2016 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 -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in -# all copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -# THE SOFTWARE. -# -# ############################################################################*/ -import copy -import unittest -import numpy -import random - -from silx.math.fit import bgtheories -from silx.math.fit.functions import sum_gauss - - -class TestBgTheories(unittest.TestCase): - """ - """ - def setUp(self): - self.x = numpy.arange(100) - self.y = 10 + 0.05 * self.x + sum_gauss(self.x, 10., 45., 15.) - # add a very narrow high amplitude peak to test strip and snip - self.y += sum_gauss(self.x, 100., 75., 2.) - self.narrow_peak_index = list(self.x).index(75) - random.seed() - - def tearDown(self): - pass - - def testTheoriesAttrs(self): - for theory_name in bgtheories.THEORY: - self.assertIsInstance(theory_name, str) - self.assertTrue(hasattr(bgtheories.THEORY[theory_name], - "function")) - self.assertTrue(hasattr(bgtheories.THEORY[theory_name].function, - "__call__")) - # Ensure legacy functions are not renamed accidentally - self.assertTrue( - {"No Background", "Constant", "Linear", "Strip", "Snip"}.issubset( - set(bgtheories.THEORY))) - - def testNoBg(self): - nobgfun = bgtheories.THEORY["No Background"].function - self.assertTrue(numpy.array_equal(nobgfun(self.x, self.y), - numpy.zeros_like(self.x))) - # default estimate - self.assertEqual(bgtheories.THEORY["No Background"].estimate(self.x, self.y), - ([], [])) - - def testConstant(self): - consfun = bgtheories.THEORY["Constant"].function - c = random.random() * 100 - self.assertTrue(numpy.array_equal(consfun(self.x, self.y, c), - c * numpy.ones_like(self.x))) - # default estimate - esti_par, cons = bgtheories.THEORY["Constant"].estimate(self.x, self.y) - self.assertEqual(cons, - [[0, 0, 0]]) - self.assertAlmostEqual(esti_par, - min(self.y)) - - def testLinear(self): - linfun = bgtheories.THEORY["Linear"].function - a = random.random() * 100 - b = random.random() * 100 - self.assertTrue(numpy.array_equal(linfun(self.x, self.y, a, b), - a + b * self.x)) - # default estimate - esti_par, cons = bgtheories.THEORY["Linear"].estimate(self.x, self.y) - - self.assertEqual(cons, - [[0, 0, 0], [0, 0, 0]]) - self.assertAlmostEqual(esti_par[0], 10, places=3) - self.assertAlmostEqual(esti_par[1], 0.05, places=3) - - def testStrip(self): - stripfun = bgtheories.THEORY["Strip"].function - anchors = sorted(random.sample(list(self.x), 4)) - anchors_indices = [list(self.x).index(a) for a in anchors] - - # we really want to strip away the narrow peak - anchors_indices_copy = copy.deepcopy(anchors_indices) - for idx in anchors_indices_copy: - if abs(idx - self.narrow_peak_index) < 5: - anchors_indices.remove(idx) - anchors.remove(self.x[idx]) - - width = 2 - niter = 1000 - bgtheories.THEORY["Strip"].configure(AnchorsList=anchors, AnchorsFlag=True) - - bg = stripfun(self.x, self.y, width, niter) - - # assert peak amplitude has been decreased - self.assertLess(bg[self.narrow_peak_index], - self.y[self.narrow_peak_index]) - - # default estimate - for i in anchors_indices: - self.assertEqual(bg[i], self.y[i]) - - # estimated parameters are equal to the default ones in the config dict - bgtheories.THEORY["Strip"].configure(StripWidth=7, StripIterations=8) - esti_par, cons = bgtheories.THEORY["Strip"].estimate(self.x, self.y) - self.assertTrue(numpy.array_equal(cons, [[3, 0, 0], [3, 0, 0]])) - self.assertEqual(esti_par, [7, 8]) - - def testSnip(self): - snipfun = bgtheories.THEORY["Snip"].function - anchors = sorted(random.sample(list(self.x), 4)) - anchors_indices = [list(self.x).index(a) for a in anchors] - - # we want to strip away the narrow peak, so remove nearby anchors - anchors_indices_copy = copy.deepcopy(anchors_indices) - for idx in anchors_indices_copy: - if abs(idx - self.narrow_peak_index) < 5: - anchors_indices.remove(idx) - anchors.remove(self.x[idx]) - - width = 16 - bgtheories.THEORY["Snip"].configure(AnchorsList=anchors, AnchorsFlag=True) - bg = snipfun(self.x, self.y, width) - - # assert peak amplitude has been decreased - self.assertLess(bg[self.narrow_peak_index], - self.y[self.narrow_peak_index], - "Snip didn't decrease the peak amplitude.") - - # anchored data must remain fixed - for i in anchors_indices: - self.assertEqual(bg[i], self.y[i]) - - # estimated parameters are equal to the default ones in the config dict - bgtheories.THEORY["Snip"].configure(SnipWidth=7) - esti_par, cons = bgtheories.THEORY["Snip"].estimate(self.x, self.y) - self.assertTrue(numpy.array_equal(cons, [[3, 0, 0]])) - self.assertEqual(esti_par, [7]) - - -test_cases = (TestBgTheories,) - - -def suite(): - loader = unittest.defaultTestLoader - test_suite = unittest.TestSuite() - for test_class in test_cases: - tests = loader.loadTestsFromTestCase(test_class) - test_suite.addTests(tests) - return test_suite - -if __name__ == '__main__': - unittest.main(defaultTest="suite") |