Python operator.add() Examples

def test_binary_operators(self):

        # skipping for now #####
        import pytest
        pytest.skip("skipping sparse binary operators test")

        def _check_inplace_op(iop, op):
            tmp = self.bseries.copy()

            expected = op(tmp, self.bseries)
            iop(tmp, self.bseries)
            tm.assert_sp_series_equal(tmp, expected)

        inplace_ops = ['add', 'sub', 'mul', 'truediv', 'floordiv', 'pow']
        for op in inplace_ops:
            _check_inplace_op(getattr(operator, "i%s" % op),
                              getattr(operator, op)) 

def test_safe_binop():
    # Test checked arithmetic routines

    ops = [
        (operator.add, 1),
        (operator.sub, 2),
        (operator.mul, 3)
    ]

    with exc_iter(ops, INT64_VALUES, INT64_VALUES) as it:
        for xop, a, b in it:
            pyop, op = xop
            c = pyop(a, b)

            if not (INT64_MIN <= c <= INT64_MAX):
                assert_raises(OverflowError, mt.extint_safe_binop, a, b, op)
            else:
                d = mt.extint_safe_binop(a, b, op)
                if c != d:
                    # assert_equal is slow
                    assert_equal(d, c) 

def test_add(Poly):
    # This checks commutation, not numerical correctness
    c1 = list(random((4,)) + .5)
    c2 = list(random((3,)) + .5)
    p1 = Poly(c1)
    p2 = Poly(c2)
    p3 = p1 + p2
    assert_poly_almost_equal(p2 + p1, p3)
    assert_poly_almost_equal(p1 + c2, p3)
    assert_poly_almost_equal(c2 + p1, p3)
    assert_poly_almost_equal(p1 + tuple(c2), p3)
    assert_poly_almost_equal(tuple(c2) + p1, p3)
    assert_poly_almost_equal(p1 + np.array(c2), p3)
    assert_poly_almost_equal(np.array(c2) + p1, p3)
    assert_raises(TypeError, op.add, p1, Poly([0], domain=Poly.domain + 1))
    assert_raises(TypeError, op.add, p1, Poly([0], window=Poly.window + 1))
    if Poly is Polynomial:
        assert_raises(TypeError, op.add, p1, Chebyshev([0]))
    else:
        assert_raises(TypeError, op.add, p1, Polynomial([0])) 

def create_internal_feed(portfolio: 'Portfolio'):

    base_symbol = portfolio.base_instrument.symbol
    sources = []

    for wallet in portfolio.wallets:
        symbol = wallet.instrument.symbol
        sources += [wallet.exchange]
        sources += [create_wallet_source(wallet, include_worth=(symbol != base_symbol))]

    worth_nodes = Condition(
        lambda node: node.name.endswith(base_symbol + ":/total") or node.name.endswith("worth")
    )(*sources)

    net_worth = Reduce(func=operator.add)(worth_nodes).rename("net_worth")

    sources += [net_worth]

    feed = DataFeed(sources).attach(portfolio)

    return feed 

def apply_index(self, i):
        time = i.to_perioddelta('D')
        # to_period rolls forward to next BDay; track and
        # reduce n where it does when rolling forward
        asper = i.to_period('B')
        if not isinstance(asper._data, np.ndarray):
            # unwrap PeriodIndex --> PeriodArray
            asper = asper._data

        if self.n > 0:
            shifted = (i.to_perioddelta('B') - time).asi8 != 0

            # Integer-array addition is deprecated, so we use
            # _time_shift directly
            roll = np.where(shifted, self.n - 1, self.n)
            shifted = asper._addsub_int_array(roll, operator.add)
        else:
            # Integer addition is deprecated, so we use _time_shift directly
            roll = self.n
            shifted = asper._time_shift(roll)

        result = shifted.to_timestamp() + time
        return result 

def test_arith(self):
        self._test_op(self.panel, operator.add)
        self._test_op(self.panel, operator.sub)
        self._test_op(self.panel, operator.mul)
        self._test_op(self.panel, operator.truediv)
        self._test_op(self.panel, operator.floordiv)
        self._test_op(self.panel, operator.pow)

        self._test_op(self.panel, lambda x, y: y + x)
        self._test_op(self.panel, lambda x, y: y - x)
        self._test_op(self.panel, lambda x, y: y * x)
        self._test_op(self.panel, lambda x, y: y / x)
        self._test_op(self.panel, lambda x, y: y ** x)

        self._test_op(self.panel, lambda x, y: x + y)  # panel + 1
        self._test_op(self.panel, lambda x, y: x - y)  # panel - 1
        self._test_op(self.panel, lambda x, y: x * y)  # panel * 1
        self._test_op(self.panel, lambda x, y: x / y)  # panel / 1
        self._test_op(self.panel, lambda x, y: x ** y)  # panel ** 1

        pytest.raises(Exception, self.panel.__add__,
                      self.panel['ItemA']) 

def attach_grad(self, grad_req='write', stype=None):
        """Attach a gradient buffer to this NDArray, so that `backward`
        can compute gradient with respect to it.

        Parameters
        ----------
        grad_req : {'write', 'add', 'null'}
            How gradient will be accumulated.
            - 'write': gradient will be overwritten on every backward.
            - 'add': gradient will be added to existing value on every backward.
            - 'null': do not compute gradient for this NDArray.
        stype : str, optional
            The storage type of the gradient array. Defaults to the same stype of this NDArray.
        """
        from . import zeros as _zeros
        if stype is not None:
            grad = _zeros(self.shape, stype=stype)
        else:
            grad = op.zeros_like(self)  # pylint: disable=undefined-variable
        grad_req = _GRAD_REQ_MAP[grad_req]
        check_call(_LIB.MXAutogradMarkVariables(
            1, ctypes.pointer(self.handle),
            ctypes.pointer(mx_uint(grad_req)),
            ctypes.pointer(grad.handle))) 

def test_arith_flex_panel(self):
        ops = ['add', 'sub', 'mul', 'div',
               'truediv', 'pow', 'floordiv', 'mod']
        if not compat.PY3:
            aliases = {}
        else:
            aliases = {'div': 'truediv'}
        self.panel = self.panel.to_panel()

        for n in [np.random.randint(-50, -1), np.random.randint(1, 50), 0]:
            for op in ops:
                alias = aliases.get(op, op)
                f = getattr(operator, alias)
                exp = f(self.panel, n)
                result = getattr(self.panel, op)(n)
                assert_panel_equal(result, exp, check_panel_type=True)

                # rops
                r_f = lambda x, y: f(y, x)
                exp = r_f(self.panel, n)
                result = getattr(self.panel, 'r' + op)(n)
                assert_panel_equal(result, exp) 

def compute_rmse(model, data, n):
    """
    Compute RMSE (Root Mean Squared Error), or square root of the average value
        of (actual rating - predicted rating)^2
    """
    predictions = model.predictAll(data.map(lambda x: (x[0], x[1])))
    predictions_ratings = predictions.map(lambda x: ((x[0], x[1]), x[2])) \
      .join(data.map(lambda x: ((x[0], x[1]), x[2]))) \
      .values()
    return sqrt(predictions_ratings.map(lambda x: (x[0] - x[1]) ** 2).reduce(add) / float(n))


##########################################################################
# Main
########################################################################## 

def test_combine_series(self):
        s = self.panel['ItemA'][:10]
        result = self.panel.add(s, axis=0)
        expected = DataFrame.add(self.panel, s, axis=0)
        assert_frame_equal(result, expected)

        s = self.panel.iloc[5]
        result = self.panel + s
        expected = DataFrame.add(self.panel, s, axis=1)
        assert_frame_equal(result, expected) 

def test_combinePanel(self):
        wp = self.panel.to_panel()
        result = self.panel.add(self.panel)
        wide_result = result.to_panel()
        assert_frame_equal(wp['ItemA'] * 2, wide_result['ItemA'])

        # one item
        result = self.panel.add(self.panel.filter(['ItemA'])) 

def test_combine_from_sequence_raises():
    # https://github.com/pandas-dev/pandas/issues/22850
    ser = pd.Series(DecimalArrayWithoutFromSequence([
        decimal.Decimal("1.0"),
        decimal.Decimal("2.0")
    ]))
    result = ser.combine(ser, operator.add)

    # note: object dtype
    expected = pd.Series([decimal.Decimal("2.0"),
                          decimal.Decimal("4.0")], dtype="object")
    tm.assert_series_equal(result, expected) 

def __add__(self, other):
        if isinstance(other, Tick):
            if type(self) == type(other):
                return type(self)(self.n + other.n)
            else:
                return _delta_to_tick(self.delta + other.delta)
        elif isinstance(other, ABCPeriod):
            return other + self
        try:
            return self.apply(other)
        except ApplyTypeError:
            return NotImplemented
        except OverflowError:
            raise OverflowError("the add operation between {self} and {other} "
                                "will overflow".format(self=self, other=other)) 

def _end_apply_index(self, dtindex):
        """
        Add self to the given DatetimeIndex, specialized for case where
        self.weekday is non-null.

        Parameters
        ----------
        dtindex : DatetimeIndex

        Returns
        -------
        result : DatetimeIndex
        """
        off = dtindex.to_perioddelta('D')

        base, mult = libfrequencies.get_freq_code(self.freqstr)
        base_period = dtindex.to_period(base)
        if not isinstance(base_period._data, np.ndarray):
            # unwrap PeriodIndex --> PeriodArray
            base_period = base_period._data

        if self.n > 0:
            # when adding, dates on end roll to next
            normed = dtindex - off + Timedelta(1, 'D') - Timedelta(1, 'ns')
            roll = np.where(base_period.to_timestamp(how='end') == normed,
                            self.n, self.n - 1)
            # integer-array addition on PeriodIndex is deprecated,
            #  so we use _addsub_int_array directly
            shifted = base_period._addsub_int_array(roll, operator.add)
            base = shifted.to_timestamp(how='end')
        else:
            # integer addition on PeriodIndex is deprecated,
            #  so we use _time_shift directly
            roll = self.n
            base = base_period._time_shift(roll).to_timestamp(how='end')

        return base + off + Timedelta(1, 'ns') - Timedelta(1, 'D') 

def test_raise_when_not_implemented(self):
        p = Panel(np.arange(3 * 4 * 5).reshape(3, 4, 5),
                  items=['ItemA', 'ItemB', 'ItemC'],
                  major_axis=date_range('20130101', periods=4),
                  minor_axis=list('ABCDE'))
        d = p.sum(axis=1).iloc[0]
        ops = ['add', 'sub', 'mul', 'truediv',
               'floordiv', 'div', 'mod', 'pow']
        for op in ops:
            with pytest.raises(NotImplementedError):
                getattr(p, op)(d, axis=0) 

def __init__(self, capacity):
        super(SumSegmentTree, self).__init__(
            capacity=capacity,
            operation=operator.add,
            neutral_element=0.0
        ) 

def __init__(self, capacity):
        super(SumSegmentTree, self).__init__(
            capacity=capacity,
            operation=operator.add,
            neutral_element=0.0
        ) 

def __init__(self, capacity):
        super(SumSegmentTree, self).__init__(
            capacity=capacity,
            operation=operator.add,
            neutral_element=0.0
        ) 

def main(sc):
    """
    Primary analysis mechanism for Spark application
    """

    # Load stopwords from the dataset
    with open('stopwords.txt', 'r') as words:
        stopwords = frozenset([
            word.strip() for word in words.read().split("\n")
        ])

    # Broadcast the stopwords across the cluster
    stopwords = sc.broadcast(stopwords)

    # Create an accumulator to count total number of trigrams and words.
    words     = sc.accumulator(0)
    trigrams  = sc.accumulator(0)

    # Load and parse the corpus from HDFS and insert into an RDD
    tokens = sc.textFile("textcorpus").flatMap(tokenize)

    # Perform the word count
    tokens.foreach(lambda w: words.add(1))

    # Filter stopwords and extract trigrams
    tokens = tokens.filter(partial(include, stopwords=stopwords))
    tokens = tokens.mapPartitions(partial(ngrams, n=3))

    # Perform the trigram count
    tokens.foreach(lambda ng: trigrams.add(1))

    # Counter per-trigram frequency
    tokens = tokens.map(lambda t: (t, 1)).reduceByKey(add)

    # Write output to disk
    tokens.saveAsTextFile("trigrams")

    print "Number of trigrams: {} in {} words.".format(trigrams.value, words.value)
    for trigram, frequency in tokens.sortBy(lambda (t,c): c).take(100):
        print "{}: {}".format(frequency, trigram) 

def main(sc):
    """
    Primary analysis mechanism for Spark application
    """

    # Load stopwords from the dataset
    with open('stopwords.txt', 'r') as words:
        stopwords = frozenset([
            word.strip() for word in words.read().split("\n")
        ])

    # Broadcast the stopwords across the cluster
    stopwords = sc.broadcast(stopwords)

    # Load the corpus as whole test files and chunk them.
    corpus  = sc.wholeTextFiles('reuters.txt').flatMap(chunk)

    # Phase one: tokenize and sum (word, docid), count pairs (document frequency).
    docfreq = corpus.flatMap(partial(tokenize, stopwords=stopwords))
    docfreq = docfreq.reduceByKey(add)

    # Phase two: compute term frequency then perform keyspace change.
    trmfreq = docfreq.map(lambda (key, tf): (key[1], (key[0], tf, 1)))
    trmfreq = trmfreq.reduceByKey(term_frequency)
    trmfreq = trmfreq.map(lambda (word, (docid, tf, n)): ((word, docid), (tf, n)))

    # Phase three: comptue the tf-idf of each word, document pair.
    tfidfs  = trmfreq.map(tfidf)

    # Write the results out to disk
    tfidfs.saveAsTextFile("reuters-tfidfs") 

def recursive_repr(fillvalue='...'):
    "Decorator to make a repr function return fillvalue for a recursive call."
    # pylint: disable=missing-docstring
    # Copied from reprlib in Python 3
    # https://hg.python.org/cpython/file/3.6/Lib/reprlib.py

    def decorating_function(user_function):
        repr_running = set()

        @wraps(user_function)
        def wrapper(self):
            key = id(self), get_ident()
            if key in repr_running:
                return fillvalue
            repr_running.add(key)
            try:
                result = user_function(self)
            finally:
                repr_running.discard(key)
            return result

        return wrapper

    return decorating_function

###############################################################################
# END Python 2/3 Shims
############################################################################### 

def compute_rmse(model, data, n):
    """
    Compute RMSE (Root Mean Squared Error), or square root of the average value
        of (actual rating - predicted rating)^2
    """
    predictions = model.predictAll(data.map(lambda x: (x[0], x[1])))
    predictions_ratings = predictions.map(lambda x: ((x[0], x[1]), x[2])) \
      .join(data.map(lambda x: ((x[0], x[1]), x[2]))) \
      .values()
    return sqrt(predictions_ratings.map(lambda x: (x[0] - x[1]) ** 2).reduce(add) / float(n))


##########################################################################
# Main
########################################################################## 

def __add__(self, other):
        return add(self, other) 

def update(self, iterable):
        """Update sorted-key list by adding all values from `iterable`.

        Runtime complexity: `O(k*log(n))` -- approximate.

        >>> from operator import neg
        >>> skl = SortedKeyList(key=neg)
        >>> skl.update([3, 1, 2])
        >>> skl
        SortedKeyList([3, 2, 1], key=<built-in function neg>)

        :param iterable: iterable of values to add

        """
        _lists = self._lists
        _keys = self._keys
        _maxes = self._maxes
        values = sorted(iterable, key=self._key)

        if _maxes:
            if len(values) * 4 >= self._len:
                values.extend(chain.from_iterable(_lists))
                values.sort(key=self._key)
                self._clear()
            else:
                _add = self.add
                for val in values:
                    _add(val)
                return

        _load = self._load
        _lists.extend(values[pos:(pos + _load)]
                      for pos in range(0, len(values), _load))
        _keys.extend(list(map(self._key, _list)) for _list in _lists)
        _maxes.extend(sublist[-1] for sublist in _keys)
        self._len = len(values)
        del self._index[:] 

def append(self, value):
        """Raise not-implemented error.

        Implemented to override `MutableSequence.append` which provides an
        erroneous default implementation.

        :raises NotImplementedError: use ``sl.add(value)`` instead

        """
        raise NotImplementedError('use ``sl.add(value)`` instead') 

def append(self, value):
        """Raise not-implemented error.

        Implemented to override `MutableSequence.append` which provides an
        erroneous default implementation.

        :raises NotImplementedError: use ``sl.add(value)`` instead

        """
        raise NotImplementedError('use ``sl.add(value)`` instead') 

def __setitem__(self, index, value):
        """Raise not-implemented error.

        ``sl.__setitem__(index, value)`` <==> ``sl[index] = value``

        :raises NotImplementedError: use ``del sl[index]`` and
            ``sl.add(value)`` instead

        """
        message = 'use ``del sl[index]`` and ``sl.add(value)`` instead'
        raise NotImplementedError(message) 

def update(self, iterable):
        """Update sorted list by adding all values from `iterable`.

        Runtime complexity: `O(k*log(n))` -- approximate.

        >>> sl = SortedList()
        >>> sl.update([3, 1, 2])
        >>> sl
        SortedList([1, 2, 3])

        :param iterable: iterable of values to add

        """
        _lists = self._lists
        _maxes = self._maxes
        values = sorted(iterable)

        if _maxes:
            if len(values) * 4 >= self._len:
                values.extend(chain.from_iterable(_lists))
                values.sort()
                self._clear()
            else:
                _add = self.add
                for val in values:
                    _add(val)
                return

        _load = self._load
        _lists.extend(values[pos:(pos + _load)]
                      for pos in range(0, len(values), _load))
        _maxes.extend(sublist[-1] for sublist in _lists)
        self._len = len(values)
        del self._index[:] 

def insert(self, index, value):
        """Raise not-implemented error.

        :raises NotImplementedError: use ``sl.add(value)`` instead

        """
        raise NotImplementedError('use ``sl.add(value)`` instead') 

def add(self, value):
        """Add `value` to sorted list.

        Runtime complexity: `O(log(n))` -- approximate.

        >>> sl = SortedList()
        >>> sl.add(3)
        >>> sl.add(1)
        >>> sl.add(2)
        >>> sl
        SortedList([1, 2, 3])

        :param value: value to add to sorted list

        """
        _lists = self._lists
        _maxes = self._maxes

        if _maxes:
            pos = bisect_right(_maxes, value)

            if pos == len(_maxes):
                pos -= 1
                _lists[pos].append(value)
                _maxes[pos] = value
            else:
                insort(_lists[pos], value)

            self._expand(pos)
        else:
            _lists.append([value])
            _maxes.append(value)

        self._len += 1