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71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
6 years ago
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"""Test deprecation and future warnings.
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"""
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from __future__ import division, absolute_import, print_function
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import numpy as np
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from numpy.testing import assert_warns
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from numpy.ma.testutils import assert_equal
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from numpy.ma.core import MaskedArrayFutureWarning
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class TestArgsort(object):
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""" gh-8701 """
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def _test_base(self, argsort, cls):
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arr_0d = np.array(1).view(cls)
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argsort(arr_0d)
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arr_1d = np.array([1, 2, 3]).view(cls)
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argsort(arr_1d)
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# argsort has a bad default for >1d arrays
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arr_2d = np.array([[1, 2], [3, 4]]).view(cls)
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result = assert_warns(
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np.ma.core.MaskedArrayFutureWarning, argsort, arr_2d)
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assert_equal(result, argsort(arr_2d, axis=None))
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# should be no warnings for explicitly specifying it
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argsort(arr_2d, axis=None)
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argsort(arr_2d, axis=-1)
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def test_function_ndarray(self):
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return self._test_base(np.ma.argsort, np.ndarray)
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def test_function_maskedarray(self):
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return self._test_base(np.ma.argsort, np.ma.MaskedArray)
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def test_method(self):
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return self._test_base(np.ma.MaskedArray.argsort, np.ma.MaskedArray)
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class TestMinimumMaximum(object):
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def test_minimum(self):
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assert_warns(DeprecationWarning, np.ma.minimum, np.ma.array([1, 2]))
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def test_maximum(self):
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assert_warns(DeprecationWarning, np.ma.maximum, np.ma.array([1, 2]))
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def test_axis_default(self):
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# NumPy 1.13, 2017-05-06
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data1d = np.ma.arange(6)
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data2d = data1d.reshape(2, 3)
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ma_min = np.ma.minimum.reduce
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ma_max = np.ma.maximum.reduce
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# check that the default axis is still None, but warns on 2d arrays
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result = assert_warns(MaskedArrayFutureWarning, ma_max, data2d)
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assert_equal(result, ma_max(data2d, axis=None))
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result = assert_warns(MaskedArrayFutureWarning, ma_min, data2d)
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assert_equal(result, ma_min(data2d, axis=None))
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# no warnings on 1d, as both new and old defaults are equivalent
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result = ma_min(data1d)
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assert_equal(result, ma_min(data1d, axis=None))
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assert_equal(result, ma_min(data1d, axis=0))
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result = ma_max(data1d)
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assert_equal(result, ma_max(data1d, axis=None))
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assert_equal(result, ma_max(data1d, axis=0))
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