Python operator.ge() Examples

def testBigComplexComparisons(self):
        self.assertFalse(F(10**23) == complex(10**23))
        self.assertRaises(TypeError, operator.gt, F(10**23), complex(10**23))
        self.assertRaises(TypeError, operator.le, F(10**23), complex(10**23))

        x = F(3, 8)
        z = complex(0.375, 0.0)
        w = complex(0.375, 0.2)
        self.assertTrue(x == z)
        self.assertFalse(x != z)
        self.assertFalse(x == w)
        self.assertTrue(x != w)
        for op in operator.lt, operator.le, operator.gt, operator.ge:
            self.assertRaises(TypeError, op, x, z)
            self.assertRaises(TypeError, op, z, x)
            self.assertRaises(TypeError, op, x, w)
            self.assertRaises(TypeError, op, w, x) 

def is_sorted(iterable, key=None, reverse=False, distinct=False):
    if key is None:
        key = identity
    if reverse:
        if distinct:
            if isinstance(iterable, range) and iterable.step < 0:
                return True
            op = operator.gt
        else:
            op = operator.ge
    else:
        if distinct:
            if isinstance(iterable, range) and iterable.step > 0:
                return True
            if isinstance(iterable, SortedFrozenSet):
                return True
            op = operator.lt
        else:
            op = operator.le
    return all(op(a, b) for a, b in pairwise(map(key, iterable))) 

def get_op(cls, op):
        ops = {
            symbol.test: cls.test,
            symbol.and_test: cls.and_test,
            symbol.atom: cls.atom,
            symbol.comparison: cls.comparison,
            'not in': lambda x, y: x not in y,
            'in': lambda x, y: x in y,
            '==': operator.eq,
            '!=': operator.ne,
            '<':  operator.lt,
            '>':  operator.gt,
            '<=': operator.le,
            '>=': operator.ge,
        }
        if hasattr(symbol, 'or_test'):
            ops[symbol.or_test] = cls.test
        return ops[op] 

def op(operation, column):
    if operation == 'in':
        def comparator(column, v):
            return column.in_(v)
    elif operation == 'like':
        def comparator(column, v):
            return column.like(v + '%')
    elif operation == 'eq':
        comparator = _operator.eq
    elif operation == 'ne':
        comparator = _operator.ne
    elif operation == 'le':
        comparator = _operator.le
    elif operation == 'lt':
        comparator = _operator.lt
    elif operation == 'ge':
        comparator = _operator.ge
    elif operation == 'gt':
        comparator = _operator.gt
    else:
        raise ValueError('Operation {} not supported'.format(operation))
    return comparator


# TODO: fix comparators, keys should be something better 

def add_globals(self):
    "Add some Scheme standard procedures."
    import math, cmath, operator as op
    from functools import reduce
    self.update(vars(math))
    self.update(vars(cmath))
    self.update({
     '+':op.add, '-':op.sub, '*':op.mul, '/':op.itruediv, 'níl':op.not_, 'agus':op.and_,
     '>':op.gt, '<':op.lt, '>=':op.ge, '<=':op.le, '=':op.eq, 'mod':op.mod, 
     'frmh':cmath.sqrt, 'dearbhluach':abs, 'uas':max, 'íos':min,
     'cothrom_le?':op.eq, 'ionann?':op.is_, 'fad':len, 'cons':cons,
     'ceann':lambda x:x[0], 'tóin':lambda x:x[1:], 'iarcheangail':op.add,  
     'liosta':lambda *x:list(x), 'liosta?': lambda x:isa(x,list),
     'folamh?':lambda x: x == [], 'adamh?':lambda x: not((isa(x, list)) or (x == None)),
     'boole?':lambda x: isa(x, bool), 'scag':lambda f, x: list(filter(f, x)),
     'cuir_le':lambda proc,l: proc(*l), 'mapáil':lambda p, x: list(map(p, x)), 
     'lódáil':lambda fn: load(fn), 'léigh':lambda f: f.read(),
     'oscail_comhad_ionchuir':open,'dún_comhad_ionchuir':lambda p: p.file.close(), 
     'oscail_comhad_aschur':lambda f:open(f,'w'), 'dún_comhad_aschur':lambda p: p.close(),
     'dac?':lambda x:x is eof_object, 'luacháil':lambda x: evaluate(x),
     'scríobh':lambda x,port=sys.stdout:port.write(to_string(x) + '\n')})
    return self 

def _filter_range_index(pd_range_index, min_val, min_val_close, max_val, max_val_close):
    if is_pd_range_empty(pd_range_index):
        return pd_range_index

    raw_min, raw_max, step = pd_range_index.min(), pd_range_index.max(), _get_range_index_step(pd_range_index)

    # seek min range
    greater_func = operator.gt if min_val_close else operator.ge
    actual_min = raw_min
    while greater_func(min_val, actual_min):
        actual_min += abs(step)
    if step < 0:
        actual_min += step  # on the right side

    # seek max range
    less_func = operator.lt if max_val_close else operator.le
    actual_max = raw_max
    while less_func(max_val, actual_max):
        actual_max -= abs(step)
    if step > 0:
        actual_max += step  # on the right side

    if step > 0:
        return pd.RangeIndex(actual_min, actual_max, step)
    return pd.RangeIndex(actual_max, actual_min, step) 

def operator_ge(self, app_data, test_data):
        """Compare app data is greater than or equal to tests data.

        Args:
            app_data (dict, list, str): The data created by the App.
            test_data (dict, list, str): The data provided in the test case.

        Returns:
            bool, str: The results of the operator and any error message
        """
        if app_data is None:
            return False, 'Invalid app_data: {app_data}'
        if test_data is None:
            return False, 'Invalid test_data: {test_data}'

        app_data = self._string_to_int_float(app_data)
        test_data = self._string_to_int_float(test_data)
        results = operator.ge(app_data, test_data)
        details = ''
        if not results:
            details = f'{app_data} {type(app_data)} !(>=) {test_data} {type(test_data)}'
        return results, details 

def test_richcompare_crash(self):
        # gh-4613
        import operator as op

        # dummy class where __array__ throws exception
        class Foo(object):
            __array_priority__ = 1002

            def __array__(self,*args,**kwargs):
                raise Exception()

        rhs = Foo()
        lhs = np.array(1)
        for f in [op.lt, op.le, op.gt, op.ge]:
            if sys.version_info[0] >= 3:
                assert_raises(TypeError, f, lhs, rhs)
            else:
                f(lhs, rhs)
        assert_(not op.eq(lhs, rhs))
        assert_(op.ne(lhs, rhs)) 

def test_comparison_flex_basic(self):
        left = pd.Series(np.random.randn(10))
        right = pd.Series(np.random.randn(10))

        tm.assert_series_equal(left.eq(right), left == right)
        tm.assert_series_equal(left.ne(right), left != right)
        tm.assert_series_equal(left.le(right), left < right)
        tm.assert_series_equal(left.lt(right), left <= right)
        tm.assert_series_equal(left.gt(right), left > right)
        tm.assert_series_equal(left.ge(right), left >= right)

        # axis
        for axis in [0, None, 'index']:
            tm.assert_series_equal(left.eq(right, axis=axis), left == right)
            tm.assert_series_equal(left.ne(right, axis=axis), left != right)
            tm.assert_series_equal(left.le(right, axis=axis), left < right)
            tm.assert_series_equal(left.lt(right, axis=axis), left <= right)
            tm.assert_series_equal(left.gt(right, axis=axis), left > right)
            tm.assert_series_equal(left.ge(right, axis=axis), left >= right)

        #
        msg = 'No axis named 1 for object type'
        for op in ['eq', 'ne', 'le', 'le', 'gt', 'ge']:
            with pytest.raises(ValueError, match=msg):
                getattr(left, op)(right, axis=1) 

def test_compare_frame(self):
        # GH#24282 check that Categorical.__cmp__(DataFrame) defers to frame
        data = ["a", "b", 2, "a"]
        cat = Categorical(data)

        df = DataFrame(cat)

        for op in [operator.eq, operator.ne, operator.ge,
                   operator.gt, operator.le, operator.lt]:
            with pytest.raises(ValueError):
                # alignment raises unless we transpose
                op(cat, df)

        result = cat == df.T
        expected = DataFrame([[True, True, True, True]])
        tm.assert_frame_equal(result, expected)

        result = cat[::-1] != df.T
        expected = DataFrame([[False, True, True, False]])
        tm.assert_frame_equal(result, expected) 

def test_richcompare_crash(self):
        # gh-4613
        import operator as op

        # dummy class where __array__ throws exception
        class Foo(object):
            __array_priority__ = 1002

            def __array__(self, *args, **kwargs):
                raise Exception()

        rhs = Foo()
        lhs = np.array(1)
        for f in [op.lt, op.le, op.gt, op.ge]:
            if sys.version_info[0] >= 3:
                assert_raises(TypeError, f, lhs, rhs)
            elif not sys.py3kwarning:
                # With -3 switch in python 2, DeprecationWarning is raised
                # which we are not interested in
                f(lhs, rhs)
        assert_(not op.eq(lhs, rhs))
        assert_(op.ne(lhs, rhs)) 

def test_richcompare_crash(self):
        # gh-4613
        import operator as op

        # dummy class where __array__ throws exception
        class Foo(object):
            __array_priority__ = 1002

            def __array__(self, *args, **kwargs):
                raise Exception()

        rhs = Foo()
        lhs = np.array(1)
        for f in [op.lt, op.le, op.gt, op.ge]:
            if sys.version_info[0] >= 3:
                assert_raises(TypeError, f, lhs, rhs)
            elif not sys.py3kwarning:
                # With -3 switch in python 2, DeprecationWarning is raised
                # which we are not interested in
                f(lhs, rhs)
        assert_(not op.eq(lhs, rhs))
        assert_(op.ne(lhs, rhs)) 

def test_energy_threshold_termination(self):
        class ExactSolver(Runnable):
            def next(self, state):
                return state.updated(
                    samples=dimod.ExactSolver().sample(state.problem))

        bqm = dimod.BinaryQuadraticModel({'a': 1}, {}, 0, dimod.SPIN)
        state = State.from_sample({'a': 1}, bqm)

        w = LoopUntilNoImprovement(ExactSolver(),
                                   key=operator.attrgetter('samples.first.energy'),
                                   terminate=partial(operator.ge, -1))
        s = w.run(state).result()
        self.assertEqual(s.samples.first.energy, -1)

        w = LoopUntilNoImprovement(ExactSolver(),
                                   key='samples.first.energy',
                                   terminate=partial(operator.ge, -1))
        s = w.run(state).result()
        self.assertEqual(s.samples.first.energy, -1)

        w = LoopUntilNoImprovement(ExactSolver(),
                                   terminate=partial(operator.ge, -1))
        s = w.run(state).result()
        self.assertEqual(s.samples.first.energy, -1) 

def get_op(cls, op):
        ops = {
            symbol.test: cls.test,
            symbol.and_test: cls.and_test,
            symbol.atom: cls.atom,
            symbol.comparison: cls.comparison,
            'not in': lambda x, y: x not in y,
            'in': lambda x, y: x in y,
            '==': operator.eq,
            '!=': operator.ne,
            '<':  operator.lt,
            '>':  operator.gt,
            '<=': operator.le,
            '>=': operator.ge,
        }
        if hasattr(symbol, 'or_test'):
            ops[symbol.or_test] = cls.test
        return ops[op] 

def test_comparison_flex_alignment(self):
        left = Series([1, 3, 2], index=list('abc'))
        right = Series([2, 2, 2], index=list('bcd'))

        exp = pd.Series([False, False, True, False], index=list('abcd'))
        assert_series_equal(left.eq(right), exp)

        exp = pd.Series([True, True, False, True], index=list('abcd'))
        assert_series_equal(left.ne(right), exp)

        exp = pd.Series([False, False, True, False], index=list('abcd'))
        assert_series_equal(left.le(right), exp)

        exp = pd.Series([False, False, False, False], index=list('abcd'))
        assert_series_equal(left.lt(right), exp)

        exp = pd.Series([False, True, True, False], index=list('abcd'))
        assert_series_equal(left.ge(right), exp)

        exp = pd.Series([False, True, False, False], index=list('abcd'))
        assert_series_equal(left.gt(right), exp) 

def test_comparison_flex_alignment_fill(self):
        left = Series([1, 3, 2], index=list('abc'))
        right = Series([2, 2, 2], index=list('bcd'))

        exp = pd.Series([False, False, True, True], index=list('abcd'))
        assert_series_equal(left.eq(right, fill_value=2), exp)

        exp = pd.Series([True, True, False, False], index=list('abcd'))
        assert_series_equal(left.ne(right, fill_value=2), exp)

        exp = pd.Series([False, False, True, True], index=list('abcd'))
        assert_series_equal(left.le(right, fill_value=0), exp)

        exp = pd.Series([False, False, False, True], index=list('abcd'))
        assert_series_equal(left.lt(right, fill_value=0), exp)

        exp = pd.Series([True, True, True, False], index=list('abcd'))
        assert_series_equal(left.ge(right, fill_value=0), exp)

        exp = pd.Series([True, True, False, False], index=list('abcd'))
        assert_series_equal(left.gt(right, fill_value=0), exp) 

def test_timestamp_compare(self):
        # make sure we can compare Timestamps on the right AND left hand side
        # GH4982
        df = DataFrame({'dates1': date_range('20010101', periods=10),
                        'dates2': date_range('20010102', periods=10),
                        'intcol': np.random.randint(1000000000, size=10),
                        'floatcol': np.random.randn(10),
                        'stringcol': list(tm.rands(10))})
        df.loc[np.random.rand(len(df)) > 0.5, 'dates2'] = pd.NaT
        ops = {'gt': 'lt', 'lt': 'gt', 'ge': 'le', 'le': 'ge', 'eq': 'eq',
               'ne': 'ne'}

        for left, right in ops.items():
            left_f = getattr(operator, left)
            right_f = getattr(operator, right)

            # no nats
            expected = left_f(df, Timestamp('20010109'))
            result = right_f(Timestamp('20010109'), df)
            assert_frame_equal(result, expected)

            # nats
            expected = left_f(df, Timestamp('nat'))
            result = right_f(Timestamp('nat'), df)
            assert_frame_equal(result, expected) 

def test_dicts(self):
        # Verify that __eq__ and __ne__ work for dicts even if the keys and
        # values don't support anything other than __eq__ and __ne__ (and
        # __hash__).  Complex numbers are a fine example of that.
        import random
        imag1a = {}
        for i in range(50):
            imag1a[random.randrange(100)*1j] = random.randrange(100)*1j
        items = imag1a.items()
        random.shuffle(items)
        imag1b = {}
        for k, v in items:
            imag1b[k] = v
        imag2 = imag1b.copy()
        imag2[k] = v + 1.0
        self.assertTrue(imag1a == imag1a)
        self.assertTrue(imag1a == imag1b)
        self.assertTrue(imag2 == imag2)
        self.assertTrue(imag1a != imag2)
        for opname in ("lt", "le", "gt", "ge"):
            for op in opmap[opname]:
                self.assertRaises(TypeError, op, imag1a, imag2) 

def test_comparison_operators_with_nas(self):
        ser = Series(bdate_range('1/1/2000', periods=10), dtype=object)
        ser[::2] = np.nan

        # test that comparisons work
        ops = ['lt', 'le', 'gt', 'ge', 'eq', 'ne']
        for op in ops:
            val = ser[5]

            f = getattr(operator, op)
            result = f(ser, val)

            expected = f(ser.dropna(), val).reindex(ser.index)

            if op == 'ne':
                expected = expected.fillna(True).astype(bool)
            else:
                expected = expected.fillna(False).astype(bool)

            assert_series_equal(result, expected)

            # fffffffuuuuuuuuuuuu
            # result = f(val, s)
            # expected = f(val, s.dropna()).reindex(s.index)
            # assert_series_equal(result, expected)

            # boolean &, |, ^ should work with object arrays and propagate NAs

        ops = ['and_', 'or_', 'xor']
        mask = ser.isna()
        for bool_op in ops:
            func = getattr(operator, bool_op)

            filled = ser.fillna(ser[0])

            result = func(ser < ser[9], ser > ser[3])

            expected = func(filled < filled[9], filled > filled[3])
            expected[mask] = False
            assert_series_equal(result, expected) 

def __init__(self, velocity):

        self._Model = None
        self._wall = None
        self._time = None

        self.velocity = velocity
        self.material = None
        if isinstance(velocity, (list, tuple)):
            self.velocityFn = fn.branching.conditional(velocity)
        else:
            self.velocityFn = fn.misc.constant(nd(velocity))

        self.wall_operators = {"left": op.le,
                          "right": op.ge,
                          "top":op.ge,
                          "bottom": op.le,
                          "front": op.ge,
                          "back": op.le}

        self.wall_direction_axis = {"left": 0,
                               "right": 0,
                               "front": 1,
                               "back": 1,
                               "top": -1,
                               "bottom": -1} 

def test_query_lex_compare_strings(self, parser, engine):
        import operator as opr

        a = Series(np.random.choice(list('abcde'), 20))
        b = Series(np.arange(a.size))
        df = DataFrame({'X': a, 'Y': b})

        ops = {'<': opr.lt, '>': opr.gt, '<=': opr.le, '>=': opr.ge}

        for op, func in ops.items():
            res = df.query('X %s "d"' % op, engine=engine, parser=parser)
            expected = df[func(df.X, 'd')]
            assert_frame_equal(res, expected) 

def test_timestamp_compare_series(self):
        # make sure we can compare Timestamps on the right AND left hand side
        # GH#4982
        ser = pd.Series(pd.date_range('20010101', periods=10), name='dates')
        s_nat = ser.copy(deep=True)

        ser[0] = pd.Timestamp('nat')
        ser[3] = pd.Timestamp('nat')

        ops = {'lt': 'gt', 'le': 'ge', 'eq': 'eq', 'ne': 'ne'}

        for left, right in ops.items():
            left_f = getattr(operator, left)
            right_f = getattr(operator, right)

            # no nats
            expected = left_f(ser, pd.Timestamp('20010109'))
            result = right_f(pd.Timestamp('20010109'), ser)
            tm.assert_series_equal(result, expected)

            # nats
            expected = left_f(ser, pd.Timestamp('nat'))
            result = right_f(pd.Timestamp('nat'), ser)
            tm.assert_series_equal(result, expected)

            # compare to timestamp with series containing nats
            expected = left_f(s_nat, pd.Timestamp('20010109'))
            result = right_f(pd.Timestamp('20010109'), s_nat)
            tm.assert_series_equal(result, expected)

            # compare to nat with series containing nats
            expected = left_f(s_nat, pd.Timestamp('nat'))
            result = right_f(pd.Timestamp('nat'), s_nat)
            tm.assert_series_equal(result, expected) 

def test_comparison_object_numeric_nas(self):
        ser = Series(np.random.randn(10), dtype=object)
        shifted = ser.shift(2)

        ops = ['lt', 'le', 'gt', 'ge', 'eq', 'ne']
        for op in ops:
            func = getattr(operator, op)

            result = func(ser, shifted)
            expected = func(ser.astype(float), shifted.astype(float))
            assert_series_equal(result, expected) 

def _add_comparison_methods(cls):
        """ add in comparison methods """
        cls.__eq__ = _make_comparison_op(operator.eq, cls)
        cls.__ne__ = _make_comparison_op(operator.ne, cls)
        cls.__lt__ = _make_comparison_op(operator.lt, cls)
        cls.__gt__ = _make_comparison_op(operator.gt, cls)
        cls.__le__ = _make_comparison_op(operator.le, cls)
        cls.__ge__ = _make_comparison_op(operator.ge, cls) 

def get_operator(self, op):
        """Get the corresponding operator"""
        operators = {
            'date_format': self.operator_date_format,
            'df': self.operator_date_format,
            'dd': self.operator_deep_diff,
            'is_url': self.operator_is_url,
            'is_date': self.operator_is_date,
            'is_number': self.operator_is_number,
            'length_eq': self.operator_length_eq,
            'leq': self.operator_length_eq,
            'eq': self.operator_eq,
            '=': self.operator_eq,
            'le': self.operator_le,
            '<=': self.operator_le,
            'lt': self.operator_lt,
            '<': self.operator_lt,
            'ge': self.operator_ge,
            '>=': self.operator_ge,
            'gt': self.operator_gt,
            '>': self.operator_gt,
            'jeq': self.operator_json_eq,
            'json_eq': self.operator_json_eq,
            'kveq': self.operator_keyvalue_eq,
            'keyvalue_eq': self.operator_keyvalue_eq,
            'ne': self.operator_ne,
            '!=': self.operator_ne,
            'rex': self.operator_regex_match,
            'skip': self.operator_skip,
        }
        return operators.get(op, None) 

def setUp(self):
    super(CoreBinaryOpsTest, self).setUp()

    self.x_probs_broadcast_tensor = array_ops.reshape(
        self.x_probs_lt.tensor, [self.x_size, 1, self.probs_size])

    self.channel_probs_broadcast_tensor = array_ops.reshape(
        self.channel_probs_lt.tensor, [1, self.channel_size, self.probs_size])

    # == and != are not element-wise for tf.Tensor, so they shouldn't be
    # elementwise for LabeledTensor, either.
    self.ops = [
        ('add', operator.add, math_ops.add, core.add),
        ('sub', operator.sub, math_ops.subtract, core.sub),
        ('mul', operator.mul, math_ops.multiply, core.mul),
        ('div', operator.truediv, math_ops.div, core.div),
        ('mod', operator.mod, math_ops.mod, core.mod),
        ('pow', operator.pow, math_ops.pow, core.pow_function),
        ('equal', None, math_ops.equal, core.equal),
        ('less', operator.lt, math_ops.less, core.less),
        ('less_equal', operator.le, math_ops.less_equal, core.less_equal),
        ('not_equal', None, math_ops.not_equal, core.not_equal),
        ('greater', operator.gt, math_ops.greater, core.greater),
        ('greater_equal', operator.ge, math_ops.greater_equal,
         core.greater_equal),
    ]
    self.test_lt_1 = self.x_probs_lt
    self.test_lt_2 = self.channel_probs_lt
    self.test_lt_1_broadcast = self.x_probs_broadcast_tensor
    self.test_lt_2_broadcast = self.channel_probs_broadcast_tensor
    self.broadcast_axes = [self.a0, self.a1, self.a3] 

def test_mixed(self):
        # check that comparisons involving Vector objects
        # which return rich results (i.e. Vectors with itemwise
        # comparison results) work
        a = Vector(range(2))
        b = Vector(range(3))
        # all comparisons should fail for different length
        for opname in opmap:
            self.checkfail(ValueError, opname, a, b)

        a = range(5)
        b = 5 * [2]
        # try mixed arguments (but not (a, b) as that won't return a bool vector)
        args = [(a, Vector(b)), (Vector(a), b), (Vector(a), Vector(b))]
        for (a, b) in args:
            self.checkequal("lt", a, b, [True,  True,  False, False, False])
            self.checkequal("le", a, b, [True,  True,  True,  False, False])
            self.checkequal("eq", a, b, [False, False, True,  False, False])
            self.checkequal("ne", a, b, [True,  True,  False, True,  True ])
            self.checkequal("gt", a, b, [False, False, False, True,  True ])
            self.checkequal("ge", a, b, [False, False, True,  True,  True ])

            for ops in opmap.itervalues():
                for op in ops:
                    # calls __nonzero__, which should fail
                    self.assertRaises(TypeError, bool, op(a, b)) 

def addSensor(self, sensor_id, threshold, operator=operator.ge, action=None):
        self.sensors[sensor_id] = {
            "threshold": threshold,
            "operator": operator,
            "action": action,
        } 

def test_ge(self):
        self.assertRaises(TypeError, operator.ge)
        self.assertRaises(TypeError, operator.ge, 1j, 2j)
        self.assertTrue(operator.ge(1, 0))
        self.assertTrue(operator.ge(1, 0.0))
        self.assertTrue(operator.ge(1, 1))
        self.assertTrue(operator.ge(1, 1.0))
        self.assertFalse(operator.ge(1, 2))
        self.assertFalse(operator.ge(1, 2.0)) 

def test_array_richcompare_legacy_weirdness(self):
        # It doesn't really work to use assert_deprecated here, b/c part of
        # the point of assert_deprecated is to check that when warnings are
        # set to "error" mode then the error is propagated -- which is good!
        # But here we are testing a bunch of code that is deprecated *because*
        # it has the habit of swallowing up errors and converting them into
        # different warnings. So assert_warns will have to be sufficient.
        assert_warns(FutureWarning, lambda: np.arange(2) == "a")
        assert_warns(FutureWarning, lambda: np.arange(2) != "a")
        # No warning for scalar comparisons
        with warnings.catch_warnings():
            warnings.filterwarnings("error")
            assert_(not (np.array(0) == "a"))
            assert_(np.array(0) != "a")
            assert_(not (np.int16(0) == "a"))
            assert_(np.int16(0) != "a")

        for arg1 in [np.asarray(0), np.int16(0)]:
            struct = np.zeros(2, dtype="i4,i4")
            for arg2 in [struct, "a"]:
                for f in [operator.lt, operator.le, operator.gt, operator.ge]:
                    if sys.version_info[0] >= 3:
                        # py3
                        with warnings.catch_warnings() as l:
                            warnings.filterwarnings("always")
                            assert_raises(TypeError, f, arg1, arg2)
                            assert_(not l)
                    else:
                        # py2
                        assert_warns(DeprecationWarning, f, arg1, arg2)