Python operator.mul() Examples

def add_flex_arithmetic_methods(cls):
    """
    Adds the full suite of flex arithmetic methods (``pow``, ``mul``, ``add``)
    to the class.

    Parameters
    ----------
    cls : class
        flex methods will be defined and pinned to this class
    """
    flex_arith_method, flex_comp_method, _, _, _ = _get_method_wrappers(cls)
    new_methods = _create_methods(cls, flex_arith_method,
                                  flex_comp_method, bool_method=None,
                                  special=False)
    new_methods.update(dict(multiply=new_methods['mul'],
                            subtract=new_methods['sub'],
                            divide=new_methods['div']))
    # opt out of bool flex methods for now
    assert not any(kname in new_methods for kname in ('ror_', 'rxor', 'rand_'))

    add_methods(cls, new_methods=new_methods)


# -----------------------------------------------------------------------------
# Series 

def _apply_window(da, dims, window_type='hanning'):
    """Creating windows in dimensions dims."""

    if window_type not in ['hanning']:
        raise NotImplementedError("Only hanning window is supported for now.")

    numpy_win_func = getattr(np, window_type)

    if da.chunks:
        def dask_win_func(n):
            return dsar.from_delayed(
                delayed(numpy_win_func, pure=True)(n),
                (n,), float)
        win_func = dask_win_func
    else:
        win_func = numpy_win_func

    windows = [xr.DataArray(win_func(len(da[d])),
               dims=da[d].dims, coords=da[d].coords) for d in dims]

    return da * reduce(operator.mul, windows[::-1]) 

def feed_forward_categorical_fun(action_space, config, observations):
  """Feed-forward categorical."""
  if not isinstance(action_space, gym.spaces.Discrete):
    raise ValueError("Expecting discrete action space.")
  flat_observations = tf.reshape(observations, [
      tf.shape(observations)[0], tf.shape(observations)[1],
      functools.reduce(operator.mul, observations.shape.as_list()[2:], 1)])
  with tf.variable_scope("network_parameters"):
    with tf.variable_scope("policy"):
      x = flat_observations
      for size in config.policy_layers:
        x = tf.contrib.layers.fully_connected(x, size, tf.nn.relu)
      logits = tf.contrib.layers.fully_connected(x, action_space.n,
                                                 activation_fn=None)
    with tf.variable_scope("value"):
      x = flat_observations
      for size in config.value_layers:
        x = tf.contrib.layers.fully_connected(x, size, tf.nn.relu)
      value = tf.contrib.layers.fully_connected(x, 1, None)[..., 0]
  policy = tf.contrib.distributions.Categorical(logits=logits)
  return NetworkOutput(policy, value, lambda a: a) 

def dense_bitwise_categorical_fun(action_space, config, observations):
  """Dense network with bitwise input and categorical output."""
  del config
  obs_shape = common_layers.shape_list(observations)
  x = tf.reshape(observations, [-1] + obs_shape[2:])

  with tf.variable_scope("network_parameters"):
    with tf.variable_scope("dense_bitwise"):
      x = discretization.int_to_bit_embed(x, 8, 32)
      flat_x = tf.reshape(
          x, [obs_shape[0], obs_shape[1],
              functools.reduce(operator.mul, x.shape.as_list()[1:], 1)])

      x = tf.contrib.layers.fully_connected(flat_x, 256, tf.nn.relu)
      x = tf.contrib.layers.fully_connected(flat_x, 128, tf.nn.relu)

      logits = tf.contrib.layers.fully_connected(x, action_space.n,
                                                 activation_fn=None)

      value = tf.contrib.layers.fully_connected(
          x, 1, activation_fn=None)[..., 0]
      policy = tf.contrib.distributions.Categorical(logits=logits)

  return NetworkOutput(policy, value, lambda a: a) 

def begin_read_samples(self):
        if self.cache:
            return

        self.input.begin_read_samples()
         # copy meta
        if self.output:
            self.output.meta = self.input.meta

        self.multipliers = {}
        self.rngs = {}

        def _mul(split):
            return reduce(operator.mul, map(lambda op: _get_multiplier(split, op), self.ops), 1)

        for split in SPLITS:
            self.multipliers[split] = _mul(split)
            self.cache[split] = self._calculate_num_samples(split)
            self.rngs[split] = self.cache[split] * [None]

        self.input.end_read_samples() 

def gather_indices_2d(x, block_shape, block_stride):
  """Getting gather indices."""
  # making an identity matrix kernel
  kernel = tf.eye(block_shape[0] * block_shape[1])
  kernel = reshape_range(kernel, 0, 1, [block_shape[0], block_shape[1], 1])
  # making indices [1, h, w, 1] to appy convs
  x_shape = common_layers.shape_list(x)
  indices = tf.range(x_shape[2] * x_shape[3])
  indices = tf.reshape(indices, [1, x_shape[2], x_shape[3], 1])
  indices = tf.nn.conv2d(
      tf.cast(indices, tf.float32),
      kernel,
      strides=[1, block_stride[0], block_stride[1], 1],
      padding="VALID")
  # making indices [num_blocks, dim] to gather
  dims = common_layers.shape_list(indices)[:3]
  if all([isinstance(dim, int) for dim in dims]):
    num_blocks = functools.reduce(operator.mul, dims, 1)
  else:
    num_blocks = tf.reduce_prod(dims)
  indices = tf.reshape(indices, [num_blocks, -1])
  return tf.cast(indices, tf.int32) 

def create_wallet_source(wallet: Wallet, include_worth=True):
    exchange_name = wallet.exchange.name
    symbol = wallet.instrument.symbol

    with Module(exchange_name + ":/" + symbol) as wallet_ds:
        free_balance = Lambda(lambda w: w.balance.as_float(), wallet, name="free")
        locked_balance = Lambda(lambda w: w.locked_balance.as_float(), wallet, name="locked")
        total_balance = Lambda(lambda w: w.total_balance.as_float(), wallet, name="total")

        nodes = [free_balance, locked_balance, total_balance]

        if include_worth:
            price = Select(lambda node: node.name.endswith(symbol))(wallet.exchange)
            worth = BinOp(operator.mul, name="worth")(price, total_balance)
            nodes += [worth]

    return wallet_ds 

def _kspace_operation(image1, image2, padding, op, shape, axes):
    """Combine two images in k-space using a given `operator`

    `image1` and `image2` are required to be `Fourier` objects and
    `op` should be an operator (either `operator.mul` for a convolution
    or `operator.truediv` for deconvolution). `shape` is the shape of the
    output image (`Fourier` instance).
    """
    if len(image1.shape) != len(image2.shape):
        msg = "Both images must have the same number of axes, got {0} and {1}"
        raise Exception(msg.format(len(image1.shape), len(image2.shape)))
    fft_shape = _get_fft_shape(image1.image, image2.image, padding, axes)
    convolved_fft = op(image1.fft(fft_shape, axes), image2.fft(fft_shape, axes))
    # why is shape not image1.shape? images are never padded
    convolved = Fourier.from_fft(convolved_fft, fft_shape, shape, axes)
    return convolved 

def convolve(image1, image2, padding=3, axes=(-2, -1)):
    """Convolve two images

    Parameters
    ----------
    image1: `Fourier`
        `Fourier` object represeting the image and it's FFT.
    image2: `Fourier`
        `Fourier` object represeting the image and it's FFT.
    padding: int
        Additional padding to use when generating the FFT
        to supress artifacts.
    """
    return _kspace_operation(
        image1, image2, padding, operator.mul, image1.shape, axes=axes
    ) 

def test_datafriendly_mul(self):
        # Test keeping data w/ (inplace) multiplication
        # Test mul w/ scalar
        x = array([1, 2, 3], mask=[0, 0, 1])
        xx = x * 2
        assert_equal(xx.data, [2, 4, 3])
        assert_equal(xx.mask, [0, 0, 1])
        # Test imul w/ scalar
        x = array([1, 2, 3], mask=[0, 0, 1])
        x *= 2
        assert_equal(x.data, [2, 4, 3])
        assert_equal(x.mask, [0, 0, 1])
        # Test mul w/ array
        x = array([1, 2, 3], mask=[0, 0, 1])
        xx = x * array([10, 20, 30], mask=[1, 0, 0])
        assert_equal(xx.data, [1, 40, 3])
        assert_equal(xx.mask, [1, 0, 1])
        # Test imul w/ array
        x = array([1, 2, 3], mask=[0, 0, 1])
        x *= array([10, 20, 30], mask=[1, 0, 0])
        assert_equal(x.data, [1, 40, 3])
        assert_equal(x.mask, [1, 0, 1]) 

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_mul(Poly):
    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_poly_almost_equal(p1 * 2, p1 * Poly([2]))
    assert_poly_almost_equal(2 * p1, p1 * Poly([2]))
    assert_raises(TypeError, op.mul, p1, Poly([0], domain=Poly.domain + 1))
    assert_raises(TypeError, op.mul, p1, Poly([0], window=Poly.window + 1))
    if Poly is Polynomial:
        assert_raises(TypeError, op.mul, p1, Chebyshev([0]))
    else:
        assert_raises(TypeError, op.mul, p1, Polynomial([0])) 

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 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 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 _add_arithmetic_ops(cls):
        cls.__add__ = cls._create_arithmetic_method(operator.add)
        cls.__radd__ = cls._create_arithmetic_method(ops.radd)
        cls.__sub__ = cls._create_arithmetic_method(operator.sub)
        cls.__rsub__ = cls._create_arithmetic_method(ops.rsub)
        cls.__mul__ = cls._create_arithmetic_method(operator.mul)
        cls.__rmul__ = cls._create_arithmetic_method(ops.rmul)
        cls.__pow__ = cls._create_arithmetic_method(operator.pow)
        cls.__rpow__ = cls._create_arithmetic_method(ops.rpow)
        cls.__mod__ = cls._create_arithmetic_method(operator.mod)
        cls.__rmod__ = cls._create_arithmetic_method(ops.rmod)
        cls.__floordiv__ = cls._create_arithmetic_method(operator.floordiv)
        cls.__rfloordiv__ = cls._create_arithmetic_method(ops.rfloordiv)
        cls.__truediv__ = cls._create_arithmetic_method(operator.truediv)
        cls.__rtruediv__ = cls._create_arithmetic_method(ops.rtruediv)
        if not PY3:
            cls.__div__ = cls._create_arithmetic_method(operator.div)
            cls.__rdiv__ = cls._create_arithmetic_method(ops.rdiv)

        cls.__divmod__ = cls._create_arithmetic_method(divmod)
        cls.__rdivmod__ = cls._create_arithmetic_method(ops.rdivmod) 

def _add_numeric_methods_binary(cls):
        """
        Add in numeric methods.
        """
        cls.__add__ = _make_arithmetic_op(operator.add, cls)
        cls.__radd__ = _make_arithmetic_op(ops.radd, cls)
        cls.__sub__ = _make_arithmetic_op(operator.sub, cls)
        cls.__rsub__ = _make_arithmetic_op(ops.rsub, cls)
        cls.__rpow__ = _make_arithmetic_op(ops.rpow, cls)
        cls.__pow__ = _make_arithmetic_op(operator.pow, cls)

        cls.__truediv__ = _make_arithmetic_op(operator.truediv, cls)
        cls.__rtruediv__ = _make_arithmetic_op(ops.rtruediv, cls)
        if not compat.PY3:
            cls.__div__ = _make_arithmetic_op(operator.div, cls)
            cls.__rdiv__ = _make_arithmetic_op(ops.rdiv, cls)

        # TODO: rmod? rdivmod?
        cls.__mod__ = _make_arithmetic_op(operator.mod, cls)
        cls.__floordiv__ = _make_arithmetic_op(operator.floordiv, cls)
        cls.__rfloordiv__ = _make_arithmetic_op(ops.rfloordiv, cls)
        cls.__divmod__ = _make_arithmetic_op(divmod, cls)
        cls.__mul__ = _make_arithmetic_op(operator.mul, cls)
        cls.__rmul__ = _make_arithmetic_op(ops.rmul, cls) 

def _get_frame_op_default_axis(name):
    """
    Only DataFrame cares about default_axis, specifically:
    special methods have default_axis=None and flex methods
    have default_axis='columns'.

    Parameters
    ----------
    name : str

    Returns
    -------
    default_axis: str or None
    """
    if name.replace('__r', '__') in ['__and__', '__or__', '__xor__']:
        # bool methods
        return 'columns'
    elif name.startswith('__'):
        # __add__, __mul__, ...
        return None
    else:
        # add, mul, ...
        return 'columns' 

def _calculate_num_samples(self, split):
        """Calculate the total number of samples after applying ops.
        """
        num_samples = self.input.num_samples(split)

        # calculate how much the samples will be augmented after going through ops
        multiplier = reduce(operator.mul, map(lambda op: _get_multiplier(split, op), self.ops), 1)

        return int(num_samples * multiplier) 

def test_arith_flex_series(self, simple_frame):
        df = simple_frame

        row = df.xs('a')
        col = df['two']
        # after arithmetic refactor, add truediv here
        ops = ['add', 'sub', 'mul', 'mod']
        for op in ops:
            f = getattr(df, op)
            op = getattr(operator, op)
            tm.assert_frame_equal(f(row), op(df, row))
            tm.assert_frame_equal(f(col, axis=0), op(df.T, col).T)

        # special case for some reason
        tm.assert_frame_equal(df.add(row, axis=None), df + row)

        # cases which will be refactored after big arithmetic refactor
        tm.assert_frame_equal(df.div(row), df / row)
        tm.assert_frame_equal(df.div(col, axis=0), (df.T / col).T)

        # broadcasting issue in GH 7325
        df = pd.DataFrame(np.arange(3 * 2).reshape((3, 2)), dtype='int64')
        expected = pd.DataFrame([[np.nan, np.inf], [1.0, 1.5], [1.0, 1.25]])
        result = df.div(df[0], axis='index')
        tm.assert_frame_equal(result, expected)

        df = pd.DataFrame(np.arange(3 * 2).reshape((3, 2)), dtype='float64')
        expected = pd.DataFrame([[np.nan, np.inf], [1.0, 1.5], [1.0, 1.25]])
        result = df.div(df[0], axis='index')
        tm.assert_frame_equal(result, expected) 

def __bool__(self):
        num_elements = reduce(operator.mul, self.shape, 1)
        if num_elements == 0:
            return False
        elif num_elements == 1:
            return bool(self.asscalar())
        else:
            raise ValueError("The truth value of an NDArray with multiple elements " \
                             "is ambiguous.") 

def test_binops(self):
        ops = [operator.add, operator.sub, operator.mul, operator.floordiv,
               operator.truediv]
        scalars = [-1, 1, 2]
        idxs = [pd.RangeIndex(0, 10, 1), pd.RangeIndex(0, 20, 2),
                pd.RangeIndex(-10, 10, 2), pd.RangeIndex(5, -5, -1)]
        self.check_binop(ops, scalars, idxs) 

def download_and_parse_mnist_file(file_name, datadir=None, force=False):
    file_name = maybe_download_mnist_file(file_name, datadir=datadir,
                                          force=force)

    # Open the file and unzip it if necessary
    if os.path.splitext(file_name)[1] == '.gz':
        open_fn = gzip.open
    else:
        open_fn = open

    # Parse the file
    with open_fn(file_name, 'rb') as file_descriptor:
        header = file_descriptor.read(4)
        assert len(header) == 4

        zeros, data_type, n_dims = struct.unpack('>HBB', header)
        assert zeros == 0

        hex_to_data_type = {
            0x08: 'B',
            0x09: 'b',
            0x0b: 'h',
            0x0c: 'i',
            0x0d: 'f',
            0x0e: 'd'}
        data_type = hex_to_data_type[data_type]

        dim_sizes = struct.unpack(
            '>' + 'I' * n_dims,
            file_descriptor.read(4 * n_dims))

        data = array.array(data_type, file_descriptor.read())
        data.byteswap()

        desired_items = functools.reduce(operator.mul, dim_sizes)
        assert len(data) == desired_items
        return np.array(data).reshape(dim_sizes) 

def getSumOfMultiples(multiples, limit):
  result = 0
  sign = 1
  for i in range(1, len(multiples) + 1):
    for x in combinations(multiples, i):
      result += sign * getSumOfMultiple(reduce(mul, x, 1), limit)
    sign *= -1
  return result 

def geometric_mean(data):
    """Return the geometric mean of data
    """

    if not data:
        raise StatisticsError('geometric_mean requires at least one data point')

    # in order to support negative or null values
    data = [x if x > 0 else math.e if x == 0 else 1.0 for x in data]

    return math.pow(math.fabs(functools.reduce(operator.mul, data)), 1.0 / len(data)) 

def mul(df, other, axis='columns', level=None, fill_value=None):
    op = DataFrameMul(axis=axis, level=level, fill_value=fill_value, lhs=df, rhs=other)
    return op(df, other) 

def nannumel(x, **kwargs):
    x_size = reduce(operator.mul, x.shape)
    xp = get_array_module(x)
    return x_size - xp.sum(xp.isnan(x), **kwargs) 

def _largest_chunk_size(nsplits):
    return reduce(operator.mul, map(max, nsplits)) 

def _chunk_number(nsplits):
    return reduce(operator.mul, map(len, nsplits)) 

def perform_read(self, split: str, index: int, n_samples: int = 1): # pylint: disable=R0914

        mul = self.multipliers[split]
        offset = int(index % mul)
        start_index = int(index/mul)
        end_index = int((index+n_samples)/mul)
        read = max(1, int(n_samples/mul) + int(min(1, index%mul)))

        res = []

        samples = self.input.read_samples(split, start_index, read)
        if self.output and self.ops:
            op1, *oprest = self.ops

            for sample in samples:
                res.extend(op1.process(sample, oprest))

        else:
            res = samples

        if self.output and self.transform:
            res = [self.output.transform(sample) for sample in res]

        for sample, i in zip(res, range(start_index, end_index)):
            if self.rngs[split][i] is None:
                self.rngs[split][i] = sample.rng
            else:
                sample.rng = self.rngs[split][i]

        res = res[offset: offset+n_samples]

        return list(map(lambda s: ((s.x, s.y), (s.meta, s.rng)), res))