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-rw-r--r--silx/math/test/test_combo.py141
1 files changed, 94 insertions, 47 deletions
diff --git a/silx/math/test/test_combo.py b/silx/math/test/test_combo.py
index 92eb8b4..b985cbd 100644
--- a/silx/math/test/test_combo.py
+++ b/silx/math/test/test_combo.py
@@ -1,6 +1,6 @@
# coding: utf-8
# /*##########################################################################
-# Copyright (C) 2016 European Synchrotron Radiation Facility
+# Copyright (C) 2016-2017 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
@@ -47,46 +47,76 @@ class TestMinMax(ParametricTestCase):
UNSIGNED_INT_DTYPES = 'uint8', 'uint16', 'uint32', 'uint64'
DTYPES = FLOATING_DTYPES + SIGNED_INT_DTYPES + UNSIGNED_INT_DTYPES
- def _test_min_max(self, data, min_positive):
- """Compare min_max with numpy for the given dataset
+ 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
"""
- result = min_max(data, min_positive)
+ 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
- minimum = numpy.nanmin(data)
- if numpy.isnan(minimum): # All NaNs
- self.assertTrue(numpy.isnan(result.minimum))
- self.assertEqual(result.argmin, 0)
+ if filtered_data.size > 0:
+ minimum = numpy.nanmin(filtered_data)
+ if numpy.isnan(minimum):
+ argmin = 0
+ else:
+ # nanargmin equivalent
+ argmin = numpy.where(data == minimum)[0][0]
- else:
- self.assertEqual(result.minimum, minimum)
+ maximum = numpy.nanmax(filtered_data)
+ if numpy.isnan(maximum):
+ argmax = 0
+ else:
+ # nanargmax equivalent
+ argmax = numpy.where(data == maximum)[0][0]
- argmin = numpy.where(data == minimum)[0][0]
- self.assertEqual(result.argmin, argmin)
+ if min_positive:
+ 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]
- maximum = numpy.nanmax(data)
- if numpy.isnan(maximum): # All NaNs
- self.assertTrue(numpy.isnan(result.maximum))
- self.assertEqual(result.argmax, 0)
+ return minimum, min_pos, maximum, argmin, argmin_pos, argmax
- else:
- self.assertEqual(result.maximum, maximum)
+ def _test_min_max(self, data, min_positive, finite=False):
+ """Compare min_max with numpy for the given dataset
- argmax = numpy.where(data == maximum)[0][0]
- self.assertEqual(result.argmax, argmax)
+ :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)
- if min_positive:
- pos_data = data[data > 0]
- if len(pos_data) > 0:
- min_pos = numpy.min(pos_data)
- argmin_pos = numpy.where(data == min_pos)[0][0]
- else:
- min_pos = None
- argmin_pos = None
- self.assertEqual(result.min_positive, min_pos)
- self.assertEqual(result.argmin_positive, argmin_pos)
+ 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."""
@@ -122,39 +152,56 @@ class TestMinMax(ParametricTestCase):
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"""
- tests = [
- (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
- ]
-
for dtype in self.FLOATING_DTYPES:
- for data in tests:
+ 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 = [
- [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
+ (-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)
+ self._test_min_max(data, min_positive=True, finite=True)
def suite():
generated by cgit v1.2.3 (git 2.25.1) at 2024-07-02 22:02:08 +0000