Python operator.iadd() Examples

def __imul__(self, num):
        """Update the sorted list with `num` shallow copies of values.

        ``sl.__imul__(num)`` <==> ``sl *= num``

        Runtime complexity: `O(n*log(n))`

        >>> sl = SortedList('abc')
        >>> sl *= 3
        >>> sl
        SortedList(['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'c'])

        :param int num: count of shallow copies
        :return: existing sorted list

        """
        values = reduce(iadd, self._lists, []) * num
        self._clear()
        self._update(values)
        return self 

def __imul__(self, num):
        """Update the sorted list with `num` shallow copies of values.

        ``sl.__imul__(num)`` <==> ``sl *= num``

        Runtime complexity: `O(n*log(n))`

        >>> sl = SortedList('abc')
        >>> sl *= 3
        >>> sl
        SortedList(['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'c'])

        :param int num: count of shallow copies
        :return: existing sorted list

        """
        values = reduce(iadd, self._lists, []) * num
        self._clear()
        self._update(values)
        return self 

def __add__(self, other):
        """Return new sorted list containing all values in both sequences.

        ``sl.__add__(other)`` <==> ``sl + other``

        Values in `other` do not need to be in sorted order.

        Runtime complexity: `O(n*log(n))`

        >>> sl1 = SortedList('bat')
        >>> sl2 = SortedList('cat')
        >>> sl1 + sl2
        SortedList(['a', 'a', 'b', 'c', 't', 't'])

        :param other: other iterable
        :return: new sorted list

        """
        values = reduce(iadd, self._lists, [])
        values.extend(other)
        return self.__class__(values) 

def __mul__(self, num):
        """Return new sorted list with `num` shallow copies of values.

        ``sl.__mul__(num)`` <==> ``sl * num``

        Runtime complexity: `O(n*log(n))`

        >>> sl = SortedList('abc')
        >>> sl * 3
        SortedList(['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'c'])

        :param int num: count of shallow copies
        :return: new sorted list

        """
        values = reduce(iadd, self._lists, []) * num
        return self.__class__(values) 

def __add__(self, other):
        """Return new sorted list containing all values in both sequences.

        ``sl.__add__(other)`` <==> ``sl + other``

        Values in `other` do not need to be in sorted order.

        Runtime complexity: `O(n*log(n))`

        >>> sl1 = SortedList('bat')
        >>> sl2 = SortedList('cat')
        >>> sl1 + sl2
        SortedList(['a', 'a', 'b', 'c', 't', 't'])

        :param other: other iterable
        :return: new sorted list

        """
        values = reduce(iadd, self._lists, [])
        values.extend(other)
        return self.__class__(values) 

def _reset(self, load):
        """Reset sorted list load factor.

        The `load` specifies the load-factor of the list. The default load
        factor of 1000 works well for lists from tens to tens-of-millions of
        values. Good practice is to use a value that is the cube root of the
        list size. With billions of elements, the best load factor depends on
        your usage. It's best to leave the load factor at the default until you
        start benchmarking.

        See :doc:`implementation` and :doc:`performance-scale` for more
        information.

        Runtime complexity: `O(n)`

        :param int load: load-factor for sorted list sublists

        """
        values = reduce(iadd, self._lists, [])
        self._clear()
        self._load = load
        self._update(values) 

def __add__(self, other):
        """Return new sorted-key list containing all values in both sequences.

        ``skl.__add__(other)`` <==> ``skl + other``

        Values in `other` do not need to be in sorted-key order.

        Runtime complexity: `O(n*log(n))`

        >>> from operator import neg
        >>> skl1 = SortedKeyList([5, 4, 3], key=neg)
        >>> skl2 = SortedKeyList([2, 1, 0], key=neg)
        >>> skl1 + skl2
        SortedKeyList([5, 4, 3, 2, 1, 0], key=<built-in function neg>)

        :param other: other iterable
        :return: new sorted-key list

        """
        values = reduce(iadd, self._lists, [])
        values.extend(other)
        return self.__class__(values, key=self._key) 

def __imul__(self, num):
        """Update the sorted list with `num` shallow copies of values.

        ``sl.__imul__(num)`` <==> ``sl *= num``

        Runtime complexity: `O(n*log(n))`

        >>> sl = SortedList('abc')
        >>> sl *= 3
        >>> sl
        SortedList(['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'c'])

        :param int num: count of shallow copies
        :return: existing sorted list

        """
        values = reduce(iadd, self._lists, []) * num
        self._clear()
        self._update(values)
        return self 

def __mul__(self, num):
        """Return new sorted list with `num` shallow copies of values.

        ``sl.__mul__(num)`` <==> ``sl * num``

        Runtime complexity: `O(n*log(n))`

        >>> sl = SortedList('abc')
        >>> sl * 3
        SortedList(['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'c'])

        :param int num: count of shallow copies
        :return: new sorted list

        """
        values = reduce(iadd, self._lists, []) * num
        return self.__class__(values) 

def __add__(self, other):
        """Return new sorted list containing all values in both sequences.

        ``sl.__add__(other)`` <==> ``sl + other``

        Values in `other` do not need to be in sorted order.

        Runtime complexity: `O(n*log(n))`

        >>> sl1 = SortedList('bat')
        >>> sl2 = SortedList('cat')
        >>> sl1 + sl2
        SortedList(['a', 'a', 'b', 'c', 't', 't'])

        :param other: other iterable
        :return: new sorted list

        """
        values = reduce(iadd, self._lists, [])
        values.extend(other)
        return self.__class__(values) 

def _reset(self, load):
        """Reset sorted list load factor.

        The `load` specifies the load-factor of the list. The default load
        factor of 1000 works well for lists from tens to tens-of-millions of
        values. Good practice is to use a value that is the cube root of the
        list size. With billions of elements, the best load factor depends on
        your usage. It's best to leave the load factor at the default until you
        start benchmarking.

        See :doc:`implementation` and :doc:`performance-scale` for more
        information.

        Runtime complexity: `O(n)`

        :param int load: load-factor for sorted list sublists

        """
        values = reduce(iadd, self._lists, [])
        self._clear()
        self._load = load
        self._update(values) 

def test_inplace_addition(self, input_tuple, expected):
        self.ureg.autoconvert_offset_to_baseunit = False
        (q1v, q1u), (q2v, q2u) = input_tuple
        # update input tuple with new values to have correct values on failure
        input_tuple = ((np.array([q1v]*2, dtype=np.float), q1u),
                       (np.array([q2v]*2, dtype=np.float), q2u))
        Q_ = self.Q_
        qin1, qin2 = input_tuple
        q1, q2 = Q_(*qin1), Q_(*qin2)
        q1_cp = copy.copy(q1)
        if expected == 'error':
            self.assertRaises(OffsetUnitCalculusError, op.iadd, q1_cp, q2)
        else:
            expected = np.array([expected[0]]*2, dtype=np.float), expected[1]
            self.assertEqual(op.iadd(q1_cp, q2).units, Q_(*expected).units)
            q1_cp = copy.copy(q1)
            self.assertQuantityAlmostEqual(op.iadd(q1_cp, q2), Q_(*expected),
                                           atol=0.01) 

def __add__(self, other):
        """Return new sorted-key list containing all values in both sequences.

        ``skl.__add__(other)`` <==> ``skl + other``

        Values in `other` do not need to be in sorted-key order.

        Runtime complexity: `O(n*log(n))`

        >>> from operator import neg
        >>> skl1 = SortedKeyList([5, 4, 3], key=neg)
        >>> skl2 = SortedKeyList([2, 1, 0], key=neg)
        >>> skl1 + skl2
        SortedKeyList([5, 4, 3, 2, 1, 0], key=<built-in function neg>)

        :param other: other iterable
        :return: new sorted-key list

        """
        values = reduce(iadd, self._lists, [])
        values.extend(other)
        return self.__class__(values, key=self._key) 

def __add__(self, other):
        """Return new sorted list containing all values in both sequences.

        ``sl.__add__(other)`` <==> ``sl + other``

        Values in `other` do not need to be in sorted order.

        Runtime complexity: `O(n*log(n))`

        >>> sl1 = SortedList('bat')
        >>> sl2 = SortedList('cat')
        >>> sl1 + sl2
        SortedList(['a', 'a', 'b', 'c', 't', 't'])

        :param other: other iterable
        :return: new sorted list

        """
        values = reduce(iadd, self._lists, [])
        values.extend(other)
        return self.__class__(values) 

def __mul__(self, num):
        """Return new sorted list with `num` shallow copies of values.

        ``sl.__mul__(num)`` <==> ``sl * num``

        Runtime complexity: `O(n*log(n))`

        >>> sl = SortedList('abc')
        >>> sl * 3
        SortedList(['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'c'])

        :param int num: count of shallow copies
        :return: new sorted list

        """
        values = reduce(iadd, self._lists, []) * num
        return self.__class__(values) 

def _reset(self, load):
        """Reset sorted list load factor.

        The `load` specifies the load-factor of the list. The default load
        factor of 1000 works well for lists from tens to tens-of-millions of
        values. Good practice is to use a value that is the cube root of the
        list size. With billions of elements, the best load factor depends on
        your usage. It's best to leave the load factor at the default until you
        start benchmarking.

        See :doc:`implementation` and :doc:`performance-scale` for more
        information.

        Runtime complexity: `O(n)`

        :param int load: load-factor for sorted list sublists

        """
        values = reduce(iadd, self._lists, [])
        self._clear()
        self._load = load
        self._update(values) 

def __add__(self, other):
        """Return new sorted-key list containing all values in both sequences.

        ``skl.__add__(other)`` <==> ``skl + other``

        Values in `other` do not need to be in sorted-key order.

        Runtime complexity: `O(n*log(n))`

        >>> from operator import neg
        >>> skl1 = SortedKeyList([5, 4, 3], key=neg)
        >>> skl2 = SortedKeyList([2, 1, 0], key=neg)
        >>> skl1 + skl2
        SortedKeyList([5, 4, 3, 2, 1, 0], key=<built-in function neg>)

        :param other: other iterable
        :return: new sorted-key list

        """
        values = reduce(iadd, self._lists, [])
        values.extend(other)
        return self.__class__(values, key=self._key) 

def _test_quantity_iadd_isub(self, unit, func):
        x = self.Q_(unit, 'centimeter')
        y = self.Q_(unit, 'inch')
        z = self.Q_(unit, 'second')
        a = self.Q_(unit, None)

        func(op.iadd, x, x, self.Q_(unit + unit, 'centimeter'))
        func(op.iadd, x, y, self.Q_(unit + 2.54 * unit, 'centimeter'))
        func(op.iadd, y, x, self.Q_(unit + unit / 2.54, 'inch'))
        func(op.iadd, a, unit, self.Q_(unit + unit, None))
        self.assertRaises(DimensionalityError, op.iadd, 10, x)
        self.assertRaises(DimensionalityError, op.iadd, x, 10)
        self.assertRaises(DimensionalityError, op.iadd, x, z)

        func(op.isub, x, x, self.Q_(unit - unit, 'centimeter'))
        func(op.isub, x, y, self.Q_(unit - 2.54, 'centimeter'))
        func(op.isub, y, x, self.Q_(unit - unit / 2.54, 'inch'))
        func(op.isub, a, unit, self.Q_(unit - unit, None))
        self.assertRaises(DimensionalityError, op.sub, 10, x)
        self.assertRaises(DimensionalityError, op.sub, x, 10)
        self.assertRaises(DimensionalityError, op.sub, x, z) 

def test_operatorerrors(self):
        f2 = self.f2
        f2p = self.f2p
        f256 = self.f256
        f19 = self.f19
        self.assertRaises(TypeError, operator.add, f2(1), f2p(2))
        self.assertRaises(TypeError, operator.iadd, f2(1), f2p(2))
        self.assertRaises(TypeError, operator.sub, f2(1), f256(2))
        self.assertRaises(TypeError, operator.isub, f2(1), f256(2))
        self.assertRaises(TypeError, operator.mul, f2(1), f19(2))
        self.assertRaises(TypeError, operator.imul, f2(1), f19(2))
        self.assertRaises(TypeError, operator.truediv, f256(1), f19(2))
        self.assertRaises(TypeError, operator.itruediv, f256(1), f19(2))
        self.assertRaises(TypeError, operator.truediv, 3.14, f19(2))
        self.assertRaises(TypeError, operator.lshift, f2(1), f2(1))
        self.assertRaises(TypeError, operator.ilshift, f2(1), f2(1))
        self.assertRaises(TypeError, operator.lshift, 1, f2(1))
        self.assertRaises(TypeError, operator.rshift, f19(1), f19(1))
        self.assertRaises(TypeError, operator.irshift, f19(1), f19(1))
        self.assertRaises(TypeError, operator.irshift, f256(1), f256(1))
        self.assertRaises(TypeError, operator.pow, f2(1), f19(2))
        self.assertRaises(TypeError, operator.pow, f19(1), 3.14) 

def __add__(self, other):
        """Return new sorted-key list containing all values in both sequences.

        ``skl.__add__(other)`` <==> ``skl + other``

        Values in `other` do not need to be in sorted-key order.

        Runtime complexity: `O(n*log(n))`

        >>> from operator import neg
        >>> skl1 = SortedKeyList([5, 4, 3], key=neg)
        >>> skl2 = SortedKeyList([2, 1, 0], key=neg)
        >>> skl1 + skl2
        SortedKeyList([5, 4, 3, 2, 1, 0], key=<built-in function neg>)

        :param other: other iterable
        :return: new sorted-key list

        """
        values = reduce(iadd, self._lists, [])
        values.extend(other)
        return self.__class__(values, key=self._key) 

def _reset(self, load):
        """Reset sorted list load factor.

        The `load` specifies the load-factor of the list. The default load
        factor of 1000 works well for lists from tens to tens-of-millions of
        values. Good practice is to use a value that is the cube root of the
        list size. With billions of elements, the best load factor depends on
        your usage. It's best to leave the load factor at the default until you
        start benchmarking.

        See :doc:`implementation` and :doc:`performance-scale` for more
        information.

        Runtime complexity: `O(n)`

        :param int load: load-factor for sorted list sublists

        """
        values = reduce(iadd, self._lists, [])
        self._clear()
        self._load = load
        self._update(values) 

def __add__(self, other):
        """Return new sorted list containing all values in both sequences.

        ``sl.__add__(other)`` <==> ``sl + other``

        Values in `other` do not need to be in sorted order.

        Runtime complexity: `O(n*log(n))`

        >>> sl1 = SortedList('bat')
        >>> sl2 = SortedList('cat')
        >>> sl1 + sl2
        SortedList(['a', 'a', 'b', 'c', 't', 't'])

        :param other: other iterable
        :return: new sorted list

        """
        values = reduce(iadd, self._lists, [])
        values.extend(other)
        return self.__class__(values) 

def __mul__(self, num):
        """Return new sorted list with `num` shallow copies of values.

        ``sl.__mul__(num)`` <==> ``sl * num``

        Runtime complexity: `O(n*log(n))`

        >>> sl = SortedList('abc')
        >>> sl * 3
        SortedList(['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'c'])

        :param int num: count of shallow copies
        :return: new sorted list

        """
        values = reduce(iadd, self._lists, []) * num
        return self.__class__(values) 

def __imul__(self, num):
        """Update the sorted list with `num` shallow copies of values.

        ``sl.__imul__(num)`` <==> ``sl *= num``

        Runtime complexity: `O(n*log(n))`

        >>> sl = SortedList('abc')
        >>> sl *= 3
        >>> sl
        SortedList(['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'c'])

        :param int num: count of shallow copies
        :return: existing sorted list

        """
        values = reduce(iadd, self._lists, []) * num
        self._clear()
        self._update(values)
        return self 

def __add__(self, other):
        """Return new sorted-key list containing all values in both sequences.

        ``skl.__add__(other)`` <==> ``skl + other``

        Values in `other` do not need to be in sorted-key order.

        Runtime complexity: `O(n*log(n))`

        >>> from operator import neg
        >>> skl1 = SortedKeyList([5, 4, 3], key=neg)
        >>> skl2 = SortedKeyList([2, 1, 0], key=neg)
        >>> skl1 + skl2
        SortedKeyList([5, 4, 3, 2, 1, 0], key=<built-in function neg>)

        :param other: other iterable
        :return: new sorted-key list

        """
        values = reduce(iadd, self._lists, [])
        values.extend(other)
        return self.__class__(values, key=self._key) 

def test_iadd(self):
        a = array.array(self.typecode, self.example[::-1])
        b = a
        a += array.array(self.typecode, 2*self.example)
        self.assert_(a is b)
        self.assertEqual(
            a,
            array.array(self.typecode, self.example[::-1]+2*self.example)
        )

        b = array.array(self.badtypecode())
        if test_support.is_jython:
            self.assertRaises(TypeError, operator.add, a, b)
            self.assertRaises(TypeError, operator.iadd, a, "bad")
        else:
            self.assertRaises(TypeError, a.__add__, b)
            self.assertRaises(TypeError, a.__iadd__, "bad") 

def __imul__(self, num):
        """Update the sorted list with `num` shallow copies of values.

        ``sl.__imul__(num)`` <==> ``sl *= num``

        Runtime complexity: `O(n*log(n))`

        >>> sl = SortedList('abc')
        >>> sl *= 3
        >>> sl
        SortedList(['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'c'])

        :param int num: count of shallow copies
        :return: existing sorted list

        """
        values = reduce(iadd, self._lists, []) * num
        self._clear()
        self._update(values)
        return self 

def __mul__(self, num):
        """Return new sorted list with `num` shallow copies of values.

        ``sl.__mul__(num)`` <==> ``sl * num``

        Runtime complexity: `O(n*log(n))`

        >>> sl = SortedList('abc')
        >>> sl * 3
        SortedList(['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'c'])

        :param int num: count of shallow copies
        :return: new sorted list

        """
        values = reduce(iadd, self._lists, []) * num
        return self.__class__(values) 

def __add__(self, other):
        """Return new sorted list containing all values in both sequences.

        ``sl.__add__(other)`` <==> ``sl + other``

        Values in `other` do not need to be in sorted order.

        Runtime complexity: `O(n*log(n))`

        >>> sl1 = SortedList('bat')
        >>> sl2 = SortedList('cat')
        >>> sl1 + sl2
        SortedList(['a', 'a', 'b', 'c', 't', 't'])

        :param other: other iterable
        :return: new sorted list

        """
        values = reduce(iadd, self._lists, [])
        values.extend(other)
        return self.__class__(values) 

def _reset(self, load):
        """Reset sorted list load factor.

        The `load` specifies the load-factor of the list. The default load
        factor of 1000 works well for lists from tens to tens-of-millions of
        values. Good practice is to use a value that is the cube root of the
        list size. With billions of elements, the best load factor depends on
        your usage. It's best to leave the load factor at the default until you
        start benchmarking.

        See :doc:`implementation` and :doc:`performance-scale` for more
        information.

        Runtime complexity: `O(n)`

        :param int load: load-factor for sorted list sublists

        """
        values = reduce(iadd, self._lists, [])
        self._clear()
        self._load = load
        self._update(values)