Python operator.neg() Examples

def _add_numeric_methods_unary(cls):
        """
        Add in numeric unary methods.
        """
        def _make_evaluate_unary(op, opstr):

            def _evaluate_numeric_unary(self):

                self._validate_for_numeric_unaryop(op, opstr)
                attrs = self._get_attributes_dict()
                attrs = self._maybe_update_attributes(attrs)
                return Index(op(self.values), **attrs)

            _evaluate_numeric_unary.__name__ = opstr
            return _evaluate_numeric_unary

        cls.__neg__ = _make_evaluate_unary(operator.neg, '__neg__')
        cls.__pos__ = _make_evaluate_unary(operator.pos, '__pos__')
        cls.__abs__ = _make_evaluate_unary(np.abs, '__abs__')
        cls.__inv__ = _make_evaluate_unary(lambda x: -x, '__inv__') 

def _add_numeric_methods_unary(cls):
        """
        Add in numeric unary methods.
        """
        def _make_evaluate_unary(op, opstr):

            def _evaluate_numeric_unary(self):

                self._validate_for_numeric_unaryop(op, opstr)
                attrs = self._get_attributes_dict()
                attrs = self._maybe_update_attributes(attrs)
                return Index(op(self.values), **attrs)

            _evaluate_numeric_unary.__name__ = opstr
            return _evaluate_numeric_unary

        cls.__neg__ = _make_evaluate_unary(operator.neg, '__neg__')
        cls.__pos__ = _make_evaluate_unary(operator.pos, '__pos__')
        cls.__abs__ = _make_evaluate_unary(np.abs, '__abs__')
        cls.__inv__ = _make_evaluate_unary(lambda x: -x, '__inv__') 

def _add_numeric_methods_unary(cls):
        """ add in numeric unary methods """

        def _make_evaluate_unary(op, opstr):

            def _evaluate_numeric_unary(self):

                self._validate_for_numeric_unaryop(op, opstr)
                attrs = self._get_attributes_dict()
                attrs = self._maybe_update_attributes(attrs)
                return Index(op(self.values), **attrs)

            return _evaluate_numeric_unary

        cls.__neg__ = _make_evaluate_unary(operator.neg, '__neg__')
        cls.__pos__ = _make_evaluate_unary(operator.pos, '__pos__')
        cls.__abs__ = _make_evaluate_unary(np.abs, '__abs__')
        cls.__inv__ = _make_evaluate_unary(lambda x: -x, '__inv__') 

def test_exceptions(self):
        a = np.ones((), dtype=np.bool_)[()]
        assert_raises(TypeError, operator.neg, a) 

def test_result(self):
        types = np.typecodes['AllInteger'] + np.typecodes['AllFloat']
        with suppress_warnings() as sup:
            sup.filter(RuntimeWarning)
            for dt in types:
                a = np.ones((), dtype=dt)[()]
                assert_equal(operator.neg(a) + a, 0) 

def test_result(self):
        types = np.typecodes['AllInteger'] + np.typecodes['AllFloat']
        with suppress_warnings() as sup:
            sup.filter(RuntimeWarning)
            for dt in types:
                a = np.ones((), dtype=dt)[()]
                assert_equal(operator.neg(a) + a, 0) 

def __neg__(self):
        return Vec2d(operator.neg(self.x), operator.neg(self.y)) 

def test_exceptions(self):
        a = np.ones((), dtype=np.bool_)[()]
        assert_raises(TypeError, operator.neg, a) 

def test_quantity_abs_round(self):

        x = self.Q_(-4.2, 'meter')
        y = self.Q_(4.2, 'meter')
        # In Python 3+ round of x is delegated to x.__round__, instead of round(x.__float__)
        # and therefore it can be properly implemented by Pint
        for fun in (abs, op.pos, op.neg) + (round, ) if PYTHON3 else ():
            zx = self.Q_(fun(x.magnitude), 'meter')
            zy = self.Q_(fun(y.magnitude), 'meter')
            rx = fun(x)
            ry = fun(y)
            self.assertEqual(rx, zx, 'while testing {0}'.format(fun))
            self.assertEqual(ry, zy, 'while testing {0}'.format(fun))
            self.assertIsNot(rx, zx, 'while testing {0}'.format(fun))
            self.assertIsNot(ry, zy, 'while testing {0}'.format(fun)) 

def negative(x):
 """
 Like -x, except that a zero dimensional numpy array input results in a numpy array return value.
 This works on garrays, numpy arrays, and numbers, preserving type (though all numbers become floats).
 """
 return _elementwise__base(x, operator.neg, operator.neg) 

def __neg__(self):
    return NonStandardInteger(operator.neg(self.val)) 

def __neg__(self):
		return sparse(zip(self.keys(), map(operator.neg, self.values()))) 

def test_map(self):
        self.assertEqual(list(map(operator.pow, range(3), range(1,7))),
                         [0**1, 1**2, 2**3])
        self.assertEqual(list(map(tupleize, 'abc', range(5))),
                         [('a',0),('b',1),('c',2)])
        self.assertEqual(list(map(tupleize, 'abc', count())),
                         [('a',0),('b',1),('c',2)])
        self.assertEqual(take(2,map(tupleize, 'abc', count())),
                         [('a',0),('b',1)])
        self.assertEqual(list(map(operator.pow, [])), [])
        self.assertRaises(TypeError, map)
        self.assertRaises(TypeError, list, map(None, range(3), range(3)))
        self.assertRaises(TypeError, map, operator.neg)
        self.assertRaises(TypeError, next, map(10, range(5)))
        self.assertRaises(ValueError, next, map(errfunc, [4], [5]))
        self.assertRaises(TypeError, next, map(onearg, [4], [5]))

        # check copy, deepcopy, pickle
        ans = [('a',0),('b',1),('c',2)]

        c = map(tupleize, 'abc', count())
        self.assertEqual(list(copy.copy(c)), ans)

        c = map(tupleize, 'abc', count())
        self.assertEqual(list(copy.deepcopy(c)), ans)

        for proto in range(pickle.HIGHEST_PROTOCOL + 1):
            c = map(tupleize, 'abc', count())
            self.pickletest(proto, c) 

def __neg__(self):
    return NonStandardInteger(operator.neg(self.val)) 

def __neg__(self):
        return self.apply_op1(operator.neg) 

def test_result(self):
        types = np.typecodes['AllInteger'] + np.typecodes['AllFloat']
        with suppress_warnings() as sup:
            sup.filter(RuntimeWarning)
            for dt in types:
                a = np.ones((), dtype=dt)[()]
                assert_equal(operator.neg(a) + a, 0) 

def test_exceptions(self):
        a = np.ones((), dtype=np.bool_)[()]
        assert_raises(TypeError, operator.neg, a) 

def test_unary_methods(self):
        array = np.array([-1, 0, 1, 2])
        array_like = ArrayLike(array)
        for op in [operator.neg,
                   operator.pos,
                   abs,
                   operator.invert]:
            _assert_equal_type_and_value(op(array_like), ArrayLike(op(array))) 

def test_min(self):
        self.assertEqual(min('123123'), '1')
        self.assertEqual(min(1, 2, 3), 1)
        self.assertEqual(min((1, 2, 3, 1, 2, 3)), 1)
        self.assertEqual(min([1, 2, 3, 1, 2, 3]), 1)

        self.assertEqual(min(1, 2, 3.0), 1)
        self.assertEqual(min(1, 2.0, 3), 1)
        self.assertEqual(min(1.0, 2, 3), 1.0)

        self.assertRaises(TypeError, min)
        self.assertRaises(TypeError, min, 42)
        self.assertRaises(ValueError, min, ())
        class BadSeq:
            def __getitem__(self, index):
                raise ValueError
        self.assertRaises(ValueError, min, BadSeq())

        for stmt in (
            "min(key=int)",                 # no args
            "min(1, key=int)",              # single arg not iterable
            "min(1, 2, keystone=int)",      # wrong keyword
            "min(1, 2, key=int, abc=int)",  # two many keywords
            "min(1, 2, key=1)",             # keyfunc is not callable
            ):
            try:
                exec_(stmt, globals())
            except TypeError:
                pass
            else:
                self.fail(stmt)

        self.assertEqual(min((1,), key=neg), 1)     # one elem iterable
        self.assertEqual(min((1,2), key=neg), 2)    # two elem iterable
        self.assertEqual(min(1, 2, key=neg), 2)     # two elems

        data = [random.randrange(200) for i in range(100)]
        keys = dict((elem, random.randrange(50)) for elem in data)
        f = keys.__getitem__
        self.assertEqual(min(data, key=f),
                         sorted(data, key=f)[0]) 

def test_max(self):
        self.assertEqual(max('123123'), '3')
        self.assertEqual(max(1, 2, 3), 3)
        self.assertEqual(max((1, 2, 3, 1, 2, 3)), 3)
        self.assertEqual(max([1, 2, 3, 1, 2, 3]), 3)

        self.assertEqual(max(1, 2, 3.0), 3.0)
        self.assertEqual(max(1, 2.0, 3), 3)
        self.assertEqual(max(1.0, 2, 3), 3)

        for stmt in (
            "max(key=int)",                 # no args
            "max(1, key=int)",              # single arg not iterable
            "max(1, 2, keystone=int)",      # wrong keyword
            "max(1, 2, key=int, abc=int)",  # two many keywords
            "max(1, 2, key=1)",             # keyfunc is not callable
            ):
            try:
                exec_(stmt, globals())
            except TypeError:
                pass
            else:
                self.fail(stmt)

        self.assertEqual(max((1,), key=neg), 1)     # one elem iterable
        self.assertEqual(max((1,2), key=neg), 1)    # two elem iterable
        self.assertEqual(max(1, 2, key=neg), 1)     # two elems

        data = [random.randrange(200) for i in range(100)]
        keys = dict((elem, random.randrange(50)) for elem in data)
        f = keys.__getitem__
        self.assertEqual(max(data, key=f),
                         sorted(reversed(data), key=f)[-1]) 

def test_max(self):
        self.assertEqual(max('123123'), '3')
        self.assertEqual(max(1, 2, 3), 3)
        self.assertEqual(max((1, 2, 3, 1, 2, 3)), 3)
        self.assertEqual(max([1, 2, 3, 1, 2, 3]), 3)

        self.assertEqual(max(1, 2, 3.0), 3.0)
        self.assertEqual(max(1, 2.0, 3), 3)
        self.assertEqual(max(1.0, 2, 3), 3)

        for stmt in (
            "max(key=int)",                 # no args
            "max(1, key=int)",              # single arg not iterable
            "max(1, 2, keystone=int)",      # wrong keyword
            "max(1, 2, key=int, abc=int)",  # two many keywords
            "max(1, 2, key=1)",             # keyfunc is not callable
            ):
            try:
                exec(stmt) in globals()
            except TypeError:
                pass
            else:
                self.fail(stmt)

        self.assertEqual(max((1,), key=neg), 1)     # one elem iterable
        self.assertEqual(max((1,2), key=neg), 1)    # two elem iterable
        self.assertEqual(max(1, 2, key=neg), 1)     # two elems

        data = [random.randrange(200) for i in range(100)]
        keys = dict((elem, random.randrange(50)) for elem in data)
        f = keys.__getitem__
        self.assertEqual(max(data, key=f),
                         sorted(reversed(data), key=f)[-1]) 

def __neg__(self):
        return self._uop(operator.neg) 

def __neg__(self):
        return self._uop(operator.neg) 

def __neg__(self):
        return self._uop(operator.neg) 

def __neg__(self):
        return self._un_op(operator.neg) 

def test_unary_operations():
    OP_MAP = {"-": operator.neg, "+": operator.pos}
    for op, func in OP_MAP.items():
        z = func(datum.xxx)
        assert repr(z) == "({}datum.xxx)".format(op) 

def test_result(self):
        types = np.typecodes['AllInteger'] + np.typecodes['AllFloat']
        with suppress_warnings() as sup:
            sup.filter(RuntimeWarning)
            for dt in types:
                a = np.ones((), dtype=dt)[()]
                assert_equal(operator.neg(a) + a, 0) 

def testNeg(self):
        self.unaryCheck(operator.neg) 

def test_unary_methods(self):
        array = np.array([-1, 0, 1, 2])
        array_like = ArrayLike(array)
        for op in [operator.neg,
                   operator.pos,
                   abs,
                   operator.invert]:
            _assert_equal_type_and_value(op(array_like), ArrayLike(op(array))) 

def execute_series_lead_lag(op, data, offset, default, **kwargs):
    func = toolz.identity if isinstance(op, ops.Lag) else operator.neg
    result = data.shift(func(1 if offset is None else offset))
    return post_lead_lag(result, default)