Python numpy.testing.assert_warns() Examples

The following are 30 code examples of numpy.testing.assert_warns(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module numpy.testing , or try the search function .
Example #1
Source File: test_deprecations.py    From coffeegrindsize with MIT License 6 votes vote down vote up 
def test_axis_default(self):
        # NumPy 1.13, 2017-05-06

        data1d = np.ma.arange(6)
        data2d = data1d.reshape(2, 3)

        ma_min = np.ma.minimum.reduce
        ma_max = np.ma.maximum.reduce

        # check that the default axis is still None, but warns on 2d arrays
        result = assert_warns(MaskedArrayFutureWarning, ma_max, data2d)
        assert_equal(result, ma_max(data2d, axis=None))

        result = assert_warns(MaskedArrayFutureWarning, ma_min, data2d)
        assert_equal(result, ma_min(data2d, axis=None))

        # no warnings on 1d, as both new and old defaults are equivalent
        result = ma_min(data1d)
        assert_equal(result, ma_min(data1d, axis=None))
        assert_equal(result, ma_min(data1d, axis=0))

        result = ma_max(data1d)
        assert_equal(result, ma_max(data1d, axis=None))
        assert_equal(result, ma_max(data1d, axis=0))
Example #2
Source File: test_deprecations.py    From elasticintel with GNU General Public License v3.0 6 votes vote down vote up 
def _test_base(self, argsort, cls):
        arr_0d = np.array(1).view(cls)
        argsort(arr_0d)

        arr_1d = np.array([1, 2, 3]).view(cls)
        argsort(arr_1d)

        # argsort has a bad default for >1d arrays
        arr_2d = np.array([[1, 2], [3, 4]]).view(cls)
        result = assert_warns(
            np.ma.core.MaskedArrayFutureWarning, argsort, arr_2d)
        assert_equal(result, argsort(arr_2d, axis=None))

        # should be no warnings for explicitly specifying it
        argsort(arr_2d, axis=None)
        argsort(arr_2d, axis=-1)
Example #3
Source File: test_io.py    From ImageFusion with MIT License 6 votes vote down vote up 
def test_invalid_raise(self):
        "Test invalid raise"
        data = ["1, 1, 1, 1, 1"] * 50
        for i in range(5):
            data[10 * i] = "2, 2, 2, 2 2"
        data.insert(0, "a, b, c, d, e")
        mdata = TextIO("\n".join(data))
        #
        kwargs = dict(delimiter=",", dtype=None, names=True)
        # XXX: is there a better way to get the return value of the callable in
        # assert_warns ?
        ret = {}

        def f(_ret={}):
            _ret['mtest'] = np.ndfromtxt(mdata, invalid_raise=False, **kwargs)
        assert_warns(ConversionWarning, f, _ret=ret)
        mtest = ret['mtest']
        assert_equal(len(mtest), 45)
        assert_equal(mtest, np.ones(45, dtype=[(_, int) for _ in 'abcde']))
        #
        mdata.seek(0)
        assert_raises(ValueError, np.ndfromtxt, mdata,
                      delimiter=",", names=True)
Example #4
Source File: test_deprecations.py    From mxnet-lambda with Apache License 2.0 6 votes vote down vote up 
def test_axis_default(self):
        # NumPy 1.13, 2017-05-06

        data1d = np.ma.arange(6)
        data2d = data1d.reshape(2, 3)

        ma_min = np.ma.minimum.reduce
        ma_max = np.ma.maximum.reduce

        # check that the default axis is still None, but warns on 2d arrays
        result = assert_warns(MaskedArrayFutureWarning, ma_max, data2d)
        assert_equal(result, ma_max(data2d, axis=None))

        result = assert_warns(MaskedArrayFutureWarning, ma_min, data2d)
        assert_equal(result, ma_min(data2d, axis=None))

        # no warnings on 1d, as both new and old defaults are equivalent
        result = ma_min(data1d)
        assert_equal(result, ma_min(data1d, axis=None))
        assert_equal(result, ma_min(data1d, axis=0))

        result = ma_max(data1d)
        assert_equal(result, ma_max(data1d, axis=None))
        assert_equal(result, ma_max(data1d, axis=0))
Example #5
Source File: test_deprecations.py    From elasticintel with GNU General Public License v3.0 6 votes vote down vote up 
def test_axis_default(self):
        # NumPy 1.13, 2017-05-06

        data1d = np.ma.arange(6)
        data2d = data1d.reshape(2, 3)

        ma_min = np.ma.minimum.reduce
        ma_max = np.ma.maximum.reduce

        # check that the default axis is still None, but warns on 2d arrays
        result = assert_warns(MaskedArrayFutureWarning, ma_max, data2d)
        assert_equal(result, ma_max(data2d, axis=None))

        result = assert_warns(MaskedArrayFutureWarning, ma_min, data2d)
        assert_equal(result, ma_min(data2d, axis=None))

        # no warnings on 1d, as both new and old defaults are equivalent
        result = ma_min(data1d)
        assert_equal(result, ma_min(data1d, axis=None))
        assert_equal(result, ma_min(data1d, axis=0))

        result = ma_max(data1d)
        assert_equal(result, ma_max(data1d, axis=None))
        assert_equal(result, ma_max(data1d, axis=0))
Example #6
Source File: test_deprecations.py    From mxnet-lambda with Apache License 2.0 6 votes vote down vote up 
def _test_base(self, argsort, cls):
        arr_0d = np.array(1).view(cls)
        argsort(arr_0d)

        arr_1d = np.array([1, 2, 3]).view(cls)
        argsort(arr_1d)

        # argsort has a bad default for >1d arrays
        arr_2d = np.array([[1, 2], [3, 4]]).view(cls)
        result = assert_warns(
            np.ma.core.MaskedArrayFutureWarning, argsort, arr_2d)
        assert_equal(result, argsort(arr_2d, axis=None))

        # should be no warnings for explicitly specifying it
        argsort(arr_2d, axis=None)
        argsort(arr_2d, axis=-1)
Example #7
Source File: test_deprecations.py    From pySINDy with MIT License 6 votes vote down vote up 
def test_axis_default(self):
        # NumPy 1.13, 2017-05-06

        data1d = np.ma.arange(6)
        data2d = data1d.reshape(2, 3)

        ma_min = np.ma.minimum.reduce
        ma_max = np.ma.maximum.reduce

        # check that the default axis is still None, but warns on 2d arrays
        result = assert_warns(MaskedArrayFutureWarning, ma_max, data2d)
        assert_equal(result, ma_max(data2d, axis=None))

        result = assert_warns(MaskedArrayFutureWarning, ma_min, data2d)
        assert_equal(result, ma_min(data2d, axis=None))

        # no warnings on 1d, as both new and old defaults are equivalent
        result = ma_min(data1d)
        assert_equal(result, ma_min(data1d, axis=None))
        assert_equal(result, ma_min(data1d, axis=0))

        result = ma_max(data1d)
        assert_equal(result, ma_max(data1d, axis=None))
        assert_equal(result, ma_max(data1d, axis=0))
Example #8
Source File: test_deprecations.py    From recruit with Apache License 2.0 6 votes vote down vote up 
def _test_base(self, argsort, cls):
        arr_0d = np.array(1).view(cls)
        argsort(arr_0d)

        arr_1d = np.array([1, 2, 3]).view(cls)
        argsort(arr_1d)

        # argsort has a bad default for >1d arrays
        arr_2d = np.array([[1, 2], [3, 4]]).view(cls)
        result = assert_warns(
            np.ma.core.MaskedArrayFutureWarning, argsort, arr_2d)
        assert_equal(result, argsort(arr_2d, axis=None))

        # should be no warnings for explicitly specifying it
        argsort(arr_2d, axis=None)
        argsort(arr_2d, axis=-1)
Example #9
Source File: test_deprecations.py    From coffeegrindsize with MIT License 6 votes vote down vote up 
def _test_base(self, argsort, cls):
        arr_0d = np.array(1).view(cls)
        argsort(arr_0d)

        arr_1d = np.array([1, 2, 3]).view(cls)
        argsort(arr_1d)

        # argsort has a bad default for >1d arrays
        arr_2d = np.array([[1, 2], [3, 4]]).view(cls)
        result = assert_warns(
            np.ma.core.MaskedArrayFutureWarning, argsort, arr_2d)
        assert_equal(result, argsort(arr_2d, axis=None))

        # should be no warnings for explicitly specifying it
        argsort(arr_2d, axis=None)
        argsort(arr_2d, axis=-1)
Example #10
Source File: test_deprecations.py    From pySINDy with MIT License 6 votes vote down vote up 
def _test_base(self, argsort, cls):
        arr_0d = np.array(1).view(cls)
        argsort(arr_0d)

        arr_1d = np.array([1, 2, 3]).view(cls)
        argsort(arr_1d)

        # argsort has a bad default for >1d arrays
        arr_2d = np.array([[1, 2], [3, 4]]).view(cls)
        result = assert_warns(
            np.ma.core.MaskedArrayFutureWarning, argsort, arr_2d)
        assert_equal(result, argsort(arr_2d, axis=None))

        # should be no warnings for explicitly specifying it
        argsort(arr_2d, axis=None)
        argsort(arr_2d, axis=-1)
Example #11
Source File: test_deprecations.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 6 votes vote down vote up 
def test_axis_default(self):
        # NumPy 1.13, 2017-05-06

        data1d = np.ma.arange(6)
        data2d = data1d.reshape(2, 3)

        ma_min = np.ma.minimum.reduce
        ma_max = np.ma.maximum.reduce

        # check that the default axis is still None, but warns on 2d arrays
        result = assert_warns(MaskedArrayFutureWarning, ma_max, data2d)
        assert_equal(result, ma_max(data2d, axis=None))

        result = assert_warns(MaskedArrayFutureWarning, ma_min, data2d)
        assert_equal(result, ma_min(data2d, axis=None))

        # no warnings on 1d, as both new and old defaults are equivalent
        result = ma_min(data1d)
        assert_equal(result, ma_min(data1d, axis=None))
        assert_equal(result, ma_min(data1d, axis=0))

        result = ma_max(data1d)
        assert_equal(result, ma_max(data1d, axis=None))
        assert_equal(result, ma_max(data1d, axis=0))
Example #12
Source File: test_deprecations.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 6 votes vote down vote up 
def _test_base(self, argsort, cls):
        arr_0d = np.array(1).view(cls)
        argsort(arr_0d)

        arr_1d = np.array([1, 2, 3]).view(cls)
        argsort(arr_1d)

        # argsort has a bad default for >1d arrays
        arr_2d = np.array([[1, 2], [3, 4]]).view(cls)
        result = assert_warns(
            np.ma.core.MaskedArrayFutureWarning, argsort, arr_2d)
        assert_equal(result, argsort(arr_2d, axis=None))

        # should be no warnings for explicitly specifying it
        argsort(arr_2d, axis=None)
        argsort(arr_2d, axis=-1)
Example #13
Source File: test_deprecations.py    From GraphicDesignPatternByPython with MIT License 6 votes vote down vote up 
def test_axis_default(self):
        # NumPy 1.13, 2017-05-06

        data1d = np.ma.arange(6)
        data2d = data1d.reshape(2, 3)

        ma_min = np.ma.minimum.reduce
        ma_max = np.ma.maximum.reduce

        # check that the default axis is still None, but warns on 2d arrays
        result = assert_warns(MaskedArrayFutureWarning, ma_max, data2d)
        assert_equal(result, ma_max(data2d, axis=None))

        result = assert_warns(MaskedArrayFutureWarning, ma_min, data2d)
        assert_equal(result, ma_min(data2d, axis=None))

        # no warnings on 1d, as both new and old defaults are equivalent
        result = ma_min(data1d)
        assert_equal(result, ma_min(data1d, axis=None))
        assert_equal(result, ma_min(data1d, axis=0))

        result = ma_max(data1d)
        assert_equal(result, ma_max(data1d, axis=None))
        assert_equal(result, ma_max(data1d, axis=0))
Example #14
Source File: test_deprecations.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 6 votes vote down vote up 
def _test_base(self, argsort, cls):
        arr_0d = np.array(1).view(cls)
        argsort(arr_0d)

        arr_1d = np.array([1, 2, 3]).view(cls)
        argsort(arr_1d)

        # argsort has a bad default for >1d arrays
        arr_2d = np.array([[1, 2], [3, 4]]).view(cls)
        result = assert_warns(
            np.ma.core.MaskedArrayFutureWarning, argsort, arr_2d)
        assert_equal(result, argsort(arr_2d, axis=None))

        # should be no warnings for explicitly specifying it
        argsort(arr_2d, axis=None)
        argsort(arr_2d, axis=-1)
Example #15
Source File: test_deprecations.py    From GraphicDesignPatternByPython with MIT License 6 votes vote down vote up 
def _test_base(self, argsort, cls):
        arr_0d = np.array(1).view(cls)
        argsort(arr_0d)

        arr_1d = np.array([1, 2, 3]).view(cls)
        argsort(arr_1d)

        # argsort has a bad default for >1d arrays
        arr_2d = np.array([[1, 2], [3, 4]]).view(cls)
        result = assert_warns(
            np.ma.core.MaskedArrayFutureWarning, argsort, arr_2d)
        assert_equal(result, argsort(arr_2d, axis=None))

        # should be no warnings for explicitly specifying it
        argsort(arr_2d, axis=None)
        argsort(arr_2d, axis=-1)
Example #16
Source File: test_io.py    From Computable with MIT License 6 votes vote down vote up 
def test_invalid_raise(self):
        "Test invalid raise"
        data = ["1, 1, 1, 1, 1"] * 50
        for i in range(5):
            data[10 * i] = "2, 2, 2, 2 2"
        data.insert(0, "a, b, c, d, e")
        mdata = TextIO("\n".join(data))
        #
        kwargs = dict(delimiter=",", dtype=None, names=True)
        # XXX: is there a better way to get the return value of the callable in
        # assert_warns ?
        ret = {}
        def f(_ret={}):
            _ret['mtest'] = np.ndfromtxt(mdata, invalid_raise=False, **kwargs)
        assert_warns(ConversionWarning, f, _ret=ret)
        mtest = ret['mtest']
        assert_equal(len(mtest), 45)
        assert_equal(mtest, np.ones(45, dtype=[(_, int) for _ in 'abcde']))
        #
        mdata.seek(0)
        assert_raises(ValueError, np.ndfromtxt, mdata,
                      delimiter=",", names=True)
Example #17
Source File: test_deprecations.py    From twitter-stock-recommendation with MIT License 6 votes vote down vote up 
def test_axis_default(self):
        # NumPy 1.13, 2017-05-06

        data1d = np.ma.arange(6)
        data2d = data1d.reshape(2, 3)

        ma_min = np.ma.minimum.reduce
        ma_max = np.ma.maximum.reduce

        # check that the default axis is still None, but warns on 2d arrays
        result = assert_warns(MaskedArrayFutureWarning, ma_max, data2d)
        assert_equal(result, ma_max(data2d, axis=None))

        result = assert_warns(MaskedArrayFutureWarning, ma_min, data2d)
        assert_equal(result, ma_min(data2d, axis=None))

        # no warnings on 1d, as both new and old defaults are equivalent
        result = ma_min(data1d)
        assert_equal(result, ma_min(data1d, axis=None))
        assert_equal(result, ma_min(data1d, axis=0))

        result = ma_max(data1d)
        assert_equal(result, ma_max(data1d, axis=None))
        assert_equal(result, ma_max(data1d, axis=0))
Example #18
Source File: test_deprecations.py    From recruit with Apache License 2.0 6 votes vote down vote up 
def test_axis_default(self):
        # NumPy 1.13, 2017-05-06

        data1d = np.ma.arange(6)
        data2d = data1d.reshape(2, 3)

        ma_min = np.ma.minimum.reduce
        ma_max = np.ma.maximum.reduce

        # check that the default axis is still None, but warns on 2d arrays
        result = assert_warns(MaskedArrayFutureWarning, ma_max, data2d)
        assert_equal(result, ma_max(data2d, axis=None))

        result = assert_warns(MaskedArrayFutureWarning, ma_min, data2d)
        assert_equal(result, ma_min(data2d, axis=None))

        # no warnings on 1d, as both new and old defaults are equivalent
        result = ma_min(data1d)
        assert_equal(result, ma_min(data1d, axis=None))
        assert_equal(result, ma_min(data1d, axis=0))

        result = ma_max(data1d)
        assert_equal(result, ma_max(data1d, axis=None))
        assert_equal(result, ma_max(data1d, axis=0))
Example #19
Source File: test_deprecations.py    From vnpy_crypto with MIT License 6 votes vote down vote up 
def _test_base(self, argsort, cls):
        arr_0d = np.array(1).view(cls)
        argsort(arr_0d)

        arr_1d = np.array([1, 2, 3]).view(cls)
        argsort(arr_1d)

        # argsort has a bad default for >1d arrays
        arr_2d = np.array([[1, 2], [3, 4]]).view(cls)
        result = assert_warns(
            np.ma.core.MaskedArrayFutureWarning, argsort, arr_2d)
        assert_equal(result, argsort(arr_2d, axis=None))

        # should be no warnings for explicitly specifying it
        argsort(arr_2d, axis=None)
        argsort(arr_2d, axis=-1)
Example #20
Source File: test_deprecations.py    From twitter-stock-recommendation with MIT License 6 votes vote down vote up 
def _test_base(self, argsort, cls):
        arr_0d = np.array(1).view(cls)
        argsort(arr_0d)

        arr_1d = np.array([1, 2, 3]).view(cls)
        argsort(arr_1d)

        # argsort has a bad default for >1d arrays
        arr_2d = np.array([[1, 2], [3, 4]]).view(cls)
        result = assert_warns(
            np.ma.core.MaskedArrayFutureWarning, argsort, arr_2d)
        assert_equal(result, argsort(arr_2d, axis=None))

        # should be no warnings for explicitly specifying it
        argsort(arr_2d, axis=None)
        argsort(arr_2d, axis=-1)
Example #21
Source File: test_deprecations.py    From vnpy_crypto with MIT License 6 votes vote down vote up 
def test_axis_default(self):
        # NumPy 1.13, 2017-05-06

        data1d = np.ma.arange(6)
        data2d = data1d.reshape(2, 3)

        ma_min = np.ma.minimum.reduce
        ma_max = np.ma.maximum.reduce

        # check that the default axis is still None, but warns on 2d arrays
        result = assert_warns(MaskedArrayFutureWarning, ma_max, data2d)
        assert_equal(result, ma_max(data2d, axis=None))

        result = assert_warns(MaskedArrayFutureWarning, ma_min, data2d)
        assert_equal(result, ma_min(data2d, axis=None))

        # no warnings on 1d, as both new and old defaults are equivalent
        result = ma_min(data1d)
        assert_equal(result, ma_min(data1d, axis=None))
        assert_equal(result, ma_min(data1d, axis=0))

        result = ma_max(data1d)
        assert_equal(result, ma_max(data1d, axis=None))
        assert_equal(result, ma_max(data1d, axis=0))
Example #22
Source File: test_io.py    From Computable with MIT License 6 votes vote down vote up 
def test_invalid_raise_with_usecols(self):
        "Test invalid_raise with usecols"
        data = ["1, 1, 1, 1, 1"] * 50
        for i in range(5):
            data[10 * i] = "2, 2, 2, 2 2"
        data.insert(0, "a, b, c, d, e")
        mdata = TextIO("\n".join(data))
        kwargs = dict(delimiter=",", dtype=None, names=True,
                      invalid_raise=False)
        # XXX: is there a better way to get the return value of the callable in
        # assert_warns ?
        ret = {}
        def f(_ret={}):
            _ret['mtest'] = np.ndfromtxt(mdata, usecols=(0, 4), **kwargs)
        assert_warns(ConversionWarning, f, _ret=ret)
        mtest = ret['mtest']
        assert_equal(len(mtest), 45)
        assert_equal(mtest, np.ones(45, dtype=[(_, int) for _ in 'ae']))
        #
        mdata.seek(0)
        mtest = np.ndfromtxt(mdata, usecols=(0, 1), **kwargs)
        assert_equal(len(mtest), 50)
        control = np.ones(50, dtype=[(_, int) for _ in 'ab'])
        control[[10 * _ for _ in range(5)]] = (2, 2)
        assert_equal(mtest, control)
Example #23
Source File: test_deprecations.py    From Mastering-Elasticsearch-7.0 with MIT License 6 votes vote down vote up 
def _test_base(self, argsort, cls):
        arr_0d = np.array(1).view(cls)
        argsort(arr_0d)

        arr_1d = np.array([1, 2, 3]).view(cls)
        argsort(arr_1d)

        # argsort has a bad default for >1d arrays
        arr_2d = np.array([[1, 2], [3, 4]]).view(cls)
        result = assert_warns(
            np.ma.core.MaskedArrayFutureWarning, argsort, arr_2d)
        assert_equal(result, argsort(arr_2d, axis=None))

        # should be no warnings for explicitly specifying it
        argsort(arr_2d, axis=None)
        argsort(arr_2d, axis=-1)
Example #24
Source File: test_deprecations.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 6 votes vote down vote up 
def test_axis_default(self):
        # NumPy 1.13, 2017-05-06

        data1d = np.ma.arange(6)
        data2d = data1d.reshape(2, 3)

        ma_min = np.ma.minimum.reduce
        ma_max = np.ma.maximum.reduce

        # check that the default axis is still None, but warns on 2d arrays
        result = assert_warns(MaskedArrayFutureWarning, ma_max, data2d)
        assert_equal(result, ma_max(data2d, axis=None))

        result = assert_warns(MaskedArrayFutureWarning, ma_min, data2d)
        assert_equal(result, ma_min(data2d, axis=None))

        # no warnings on 1d, as both new and old defaults are equivalent
        result = ma_min(data1d)
        assert_equal(result, ma_min(data1d, axis=None))
        assert_equal(result, ma_min(data1d, axis=0))

        result = ma_max(data1d)
        assert_equal(result, ma_max(data1d, axis=None))
        assert_equal(result, ma_max(data1d, axis=0))
Example #25
Source File: test_deprecations.py    From Mastering-Elasticsearch-7.0 with MIT License 6 votes vote down vote up 
def test_axis_default(self):
        # NumPy 1.13, 2017-05-06

        data1d = np.ma.arange(6)
        data2d = data1d.reshape(2, 3)

        ma_min = np.ma.minimum.reduce
        ma_max = np.ma.maximum.reduce

        # check that the default axis is still None, but warns on 2d arrays
        result = assert_warns(MaskedArrayFutureWarning, ma_max, data2d)
        assert_equal(result, ma_max(data2d, axis=None))

        result = assert_warns(MaskedArrayFutureWarning, ma_min, data2d)
        assert_equal(result, ma_min(data2d, axis=None))

        # no warnings on 1d, as both new and old defaults are equivalent
        result = ma_min(data1d)
        assert_equal(result, ma_min(data1d, axis=None))
        assert_equal(result, ma_min(data1d, axis=0))

        result = ma_max(data1d)
        assert_equal(result, ma_max(data1d, axis=None))
        assert_equal(result, ma_max(data1d, axis=0))
Example #26
Source File: test_deprecations.py    From coffeegrindsize with MIT License 5 votes vote down vote up 
def test_minimum(self):
        assert_warns(DeprecationWarning, np.ma.minimum, np.ma.array([1, 2]))
Example #27
Source File: test_deprecations.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
def test_minimum(self):
        assert_warns(DeprecationWarning, np.ma.minimum, np.ma.array([1, 2]))
Example #28
Source File: test_deprecations.py    From coffeegrindsize with MIT License 5 votes vote down vote up 
def test_maximum(self):
        assert_warns(DeprecationWarning, np.ma.maximum, np.ma.array([1, 2]))
Example #29
Source File: test_deprecations.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_minimum(self):
        assert_warns(DeprecationWarning, np.ma.minimum, np.ma.array([1, 2]))
Example #30
Source File: test_deprecations.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
def test_maximum(self):
        assert_warns(DeprecationWarning, np.ma.maximum, np.ma.array([1, 2]))