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# coding: utf-8
# /*##########################################################################
# 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
# 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__ = "19/10/2017"
import unittest
import numpy
from silx.test.utils 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:
minimum = numpy.nanmin(filtered_data)
if numpy.isnan(minimum):
argmin = 0
else:
# nanargmin equivalent
argmin = numpy.where(data == minimum)[0][0]
maximum = numpy.nanmax(filtered_data)
if numpy.isnan(maximum):
argmax = 0
else:
# nanargmax equivalent
argmax = numpy.where(data == maximum)[0][0]
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]
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")
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