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Diffstat (limited to 'silx/math/fit/test/test_filters.py')
| -rw-r--r-- | silx/math/fit/test/test_filters.py | 137 |
1 files changed, 0 insertions, 137 deletions
diff --git a/silx/math/fit/test/test_filters.py b/silx/math/fit/test/test_filters.py deleted file mode 100644 index 078b998..0000000 --- a/silx/math/fit/test/test_filters.py +++ /dev/null @@ -1,137 +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 numpy -import unittest -from silx.math.fit import filters -from silx.math.fit import functions -from silx.test.utils import add_relative_noise - - -class TestSmooth(unittest.TestCase): - """ - Unit tests of smoothing functions. - - Test that the difference between a synthetic curve with 5% added random - noise and the result of smoothing that signal is less than 5%. We compare - the sum of all samples in each curve. - """ - def setUp(self): - x = numpy.arange(5000) - # (height1, center1, fwhm1, beamfwhm...) - slit_params = (50, 500, 200, 100, - 50, 600, 80, 30, - 20, 2000, 150, 150, - 50, 2250, 110, 100, - 40, 3000, 50, 10, - 23, 4980, 250, 20) - - self.y1 = functions.sum_slit(x, *slit_params) - # 5% noise - self.y1 = add_relative_noise(self.y1, 5.) - - # (height1, center1, fwhm1...) - step_params = (50, 500, 200, - 50, 600, 80, - 20, 2000, 150, - 50, 2250, 110, - 40, 3000, 50, - 23, 4980, 250,) - - self.y2 = functions.sum_stepup(x, *step_params) - # 5% noise - self.y2 = add_relative_noise(self.y2, 5.) - - self.y3 = functions.sum_stepdown(x, *step_params) - # 5% noise - self.y3 = add_relative_noise(self.y3, 5.) - - def tearDown(self): - pass - - def testSavitskyGolay(self): - npts = 25 - for y in [self.y1, self.y2, self.y3]: - smoothed_y = filters.savitsky_golay(y, npoints=npts) - - # we added +-5% of random noise. The difference must be much lower - # than 5%. - diff = abs(sum(smoothed_y) - sum(y)) / sum(y) - self.assertLess(diff, 0.05, - "Difference between data with 5%% noise and " + - "smoothed data is > 5%% (%f %%)" % (diff * 100)) - - # Try various smoothing levels - npts += 25 - - def testSmooth1d(self): - """Test the 1D smoothing against the formula - ys[i] = (y[i-1] + 2 * y[i] + y[i+1]) / 4 (for 1 < i < n-1)""" - smoothed_y = filters.smooth1d(self.y1) - - for i in range(1, len(self.y1) - 1): - self.assertAlmostEqual(4 * smoothed_y[i], - self.y1[i-1] + 2 * self.y1[i] + self.y1[i+1]) - - def testSmooth2d(self): - """Test that a 2D smoothing is the same as two successive and - orthogonal 1D smoothings""" - x = numpy.arange(10000) - - noise = 2 * numpy.random.random(10000) - 1 - noise *= 0.05 - y = x * (1 + noise) - - y.shape = (100, 100) - - smoothed_y = filters.smooth2d(y) - - intermediate_smooth = numpy.zeros_like(y) - expected_smooth = numpy.zeros_like(y) - # smooth along first dimension - for i in range(0, y.shape[0]): - intermediate_smooth[i, :] = filters.smooth1d(y[i, :]) - - # smooth along second dimension - for j in range(0, y.shape[1]): - expected_smooth[:, j] = filters.smooth1d(intermediate_smooth[:, j]) - - for i in range(0, y.shape[0]): - for j in range(0, y.shape[1]): - self.assertAlmostEqual(smoothed_y[i, j], - expected_smooth[i, j]) - - -test_cases = (TestSmooth,) - - -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") |