Method __add__

tf_quant_finance/datetime/date_tensor.py:294–345

Adds a tensor of periods. When adding months or years, the resulting day of the month is decreased to the largest valid value if necessary. E.g. 31.03.2020 + 1 month = 30.04.2020, 29.02.2020 + 1 year = 28.02.2021. Args: period_tensor: A `PeriodTensor` object broadcastable to

(self, period_tensor)

    return tf.rank(self._ordinals)

  def __add__(self, period_tensor):
    """Adds a tensor of periods.

    When adding months or years, the resulting day of the month is decreased
    to the largest valid value if necessary. E.g. 31.03.2020 + 1 month =
    30.04.2020, 29.02.2020 + 1 year = 28.02.2021.

    Args:
      period_tensor: A `PeriodTensor` object broadcastable to the shape of
      "self".

    Returns:
      The new instance of DateTensor.

    #### Example
    ```python
    dates = tff.datetime.dates_from_tuples([(2020, 2, 25), (2020, 3, 31)])
    new_dates = dates + tff.datetime.month()
    # DateTensor([(2020, 3, 25), (2020, 4, 30)])

    new_dates = dates + tff.datetime.month([1, 2])
    # DateTensor([(2020, 3, 25), (2020, 5, 31)])
    ```
    """
    period_type = period_tensor.period_type()

    if period_type == constants.PeriodType.DAY:
      ordinals = self._ordinals + period_tensor.quantity()
      return from_ordinals(ordinals)

    if period_type == constants.PeriodType.WEEK:
      return self + periods.PeriodTensor(period_tensor.quantity() * 7,
                                         constants.PeriodType.DAY)

    def adjust_day(year, month, day):
      return tf.math.minimum(day, _num_days_in_month(month, year))

    if period_type == constants.PeriodType.MONTH:
      m = self._months - 1 + period_tensor.quantity()
      y = self._years + m // 12
      m = m % 12 + 1
      d = adjust_day(y, m, self._days)
      return from_year_month_day(y, m, d, validate=False)

    if period_type == constants.PeriodType.YEAR:
      y = self._years + period_tensor.quantity()
      # Use tf.shape to handle the case of dynamically shaped `y`
      m = tf.broadcast_to(self._months, tf.shape(y))
      d = adjust_day(y, m, self._days)
      return from_year_month_day(y, m, d, validate=False)

    raise ValueError("Unrecognized period type: {}".format(period_type))

  def __sub__(self, period_tensor):
    """Subtracts a tensor of periods.