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Diffstat (limited to 'silx/math/test/test_combo.py')
| -rw-r--r-- | silx/math/test/test_combo.py | 218 |
1 files changed, 0 insertions, 218 deletions
diff --git a/silx/math/test/test_combo.py b/silx/math/test/test_combo.py deleted file mode 100644 index 1335763..0000000 --- a/silx/math/test/test_combo.py +++ /dev/null @@ -1,218 +0,0 @@ -# coding: utf-8 -# /*########################################################################## -# 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 -# 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. -# -# ############################################################################*/ -"""Tests of the combo module""" - -from __future__ import division - -__authors__ = ["T. Vincent"] -__license__ = "MIT" -__date__ = "17/01/2018" - - -import unittest - -import numpy - -from silx.utils.testutils import ParametricTestCase - -from silx.math.combo import min_max - - -class TestMinMax(ParametricTestCase): - """Tests of min max combo""" - - FLOATING_DTYPES = 'float32', 'float64' - if hasattr(numpy, "float128"): - FLOATING_DTYPES += ('float128',) - SIGNED_INT_DTYPES = 'int8', 'int16', 'int32', 'int64' - UNSIGNED_INT_DTYPES = 'uint8', 'uint16', 'uint32', 'uint64' - DTYPES = FLOATING_DTYPES + SIGNED_INT_DTYPES + UNSIGNED_INT_DTYPES - - def _numpy_min_max(self, data, min_positive=False, finite=False): - """Reference numpy implementation of min_max - - :param numpy.ndarray data: Data set to use for test - :param bool min_positive: True to test with positive min - :param bool finite: True to only test finite values - """ - data = numpy.array(data, copy=False) - if data.size == 0: - raise ValueError('Zero-sized array') - - minimum = None - argmin = None - maximum = None - argmax = None - min_pos = None - argmin_pos = None - - if finite: - filtered_data = data[numpy.isfinite(data)] - else: - filtered_data = data - - if filtered_data.size > 0: - if numpy.all(numpy.isnan(filtered_data)): - minimum = numpy.nan - argmin = 0 - maximum = numpy.nan - argmax = 0 - else: - minimum = numpy.nanmin(filtered_data) - # nanargmin equivalent - argmin = numpy.where(data == minimum)[0][0] - maximum = numpy.nanmax(filtered_data) - # nanargmax equivalent - argmax = numpy.where(data == maximum)[0][0] - - if min_positive: - with numpy.errstate(invalid='ignore'): - # Ignore invalid value encountered in greater - pos_data = filtered_data[filtered_data > 0] - if pos_data.size > 0: - min_pos = numpy.min(pos_data) - argmin_pos = numpy.where(data == min_pos)[0][0] - - return minimum, min_pos, maximum, argmin, argmin_pos, argmax - - def _test_min_max(self, data, min_positive, finite=False): - """Compare min_max with numpy for the given dataset - - :param numpy.ndarray data: Data set to use for test - :param bool min_positive: True to test with positive min - :param bool finite: True to only test finite values - """ - minimum, min_pos, maximum, argmin, argmin_pos, argmax = \ - self._numpy_min_max(data, min_positive, finite) - - result = min_max(data, min_positive, finite) - - self.assertSimilar(minimum, result.minimum) - self.assertSimilar(min_pos, result.min_positive) - self.assertSimilar(maximum, result.maximum) - self.assertSimilar(argmin, result.argmin) - self.assertSimilar(argmin_pos, result.argmin_positive) - self.assertSimilar(argmax, result.argmax) - - def assertSimilar(self, a, b): - """Assert that a and b are both None or NaN or that a == b.""" - self.assertTrue((a is None and b is None) or - (numpy.isnan(a) and numpy.isnan(b)) or - a == b) - - def test_different_datasets(self): - """Test min_max with different numpy.arange datasets.""" - size = 1000 - - for dtype in self.DTYPES: - - tests = { - '0 to N': (0, 1), - 'N-1 to 0': (size - 1, -1)} - if dtype not in self.UNSIGNED_INT_DTYPES: - tests['N/2 to -N/2'] = size // 2, -1 - tests['0 to -N'] = 0, -1 - - for name, (start, step) in tests.items(): - for min_positive in (True, False): - with self.subTest(dtype=dtype, - min_positive=min_positive, - data=name): - data = numpy.arange( - start, start + step * size, step, dtype=dtype) - - self._test_min_max(data, min_positive) - - def test_nodata(self): - """Test min_max with None and empty array""" - for dtype in self.DTYPES: - with self.subTest(dtype=dtype): - with self.assertRaises(TypeError): - min_max(None) - - data = numpy.array((), dtype=dtype) - with self.assertRaises(ValueError): - min_max(data) - - NAN_TEST_DATA = [ - (float('nan'), float('nan')), # All NaNs - (float('nan'), 1.0), # NaN first and positive - (float('nan'), -1.0), # NaN first and negative - (1.0, 2.0, float('nan')), # NaN last and positive - (-1.0, -2.0, float('nan')), # NaN last and negative - (1.0, float('nan'), -1.0), # Some NaN - ] - - def test_nandata(self): - """Test min_max with NaN in data""" - for dtype in self.FLOATING_DTYPES: - for data in self.NAN_TEST_DATA: - with self.subTest(dtype=dtype, data=data): - data = numpy.array(data, dtype=dtype) - self._test_min_max(data, min_positive=True) - - INF_TEST_DATA = [ - [float('inf')] * 3, # All +inf - [float('-inf')] * 3, # All -inf - (float('inf'), float('-inf')), # + and - inf - (float('inf'), float('-inf'), float('nan')), # +/-inf, nan last - (float('nan'), float('-inf'), float('inf')), # +/-inf, nan first - (float('inf'), float('nan'), float('-inf')), # +/-inf, nan center - ] - - def test_infdata(self): - """Test min_max with inf.""" - for dtype in self.FLOATING_DTYPES: - for data in self.INF_TEST_DATA: - with self.subTest(dtype=dtype, data=data): - data = numpy.array(data, dtype=dtype) - self._test_min_max(data, min_positive=True) - - def test_finite(self): - """Test min_max with finite=True""" - tests = [ - (-1., 2., 0.), # Basic test - (float('nan'), float('inf'), float('-inf')), # NaN + Inf - (float('nan'), float('inf'), -2, float('-inf')), # NaN + Inf + 1 value - (float('inf'), -3, -2), # values + inf - ] - tests += self.INF_TEST_DATA - tests += self.NAN_TEST_DATA - - for dtype in self.FLOATING_DTYPES: - for data in tests: - with self.subTest(dtype=dtype, data=data): - data = numpy.array(data, dtype=dtype) - self._test_min_max(data, min_positive=True, finite=True) - - -def suite(): - test_suite = unittest.TestSuite() - test_suite.addTests( - unittest.defaultTestLoader.loadTestsFromTestCase(TestMinMax)) - return test_suite - - -if __name__ == '__main__': - unittest.main(defaultTest="suite") |