pastas.stats.signatures.recession_constant
==========================================

.. py:function:: pastas.stats.signatures.recession_constant(series: pandas.Series, bins: int = 300, normalize: bool = False, min_event_length: int = 10, min_n_events: int = 2) -> float

   
   Recession constant adapted after :cite:t:`kirchner_catchments_2009`.

   :param series: Pandas Series with DatetimeIndex and head values.
   :type series: pandas.Series
   :param bins: Number of bins to bin the data to.
   :type bins: int, optional
   :param normalize: normalize the time series to values between zero and one.
   :type normalize: bool, optional
   :param min_event_length: Minimum length of an event in days. Events shorter than this are discarded.
   :type min_event_length: int, optional
   :param min_n_events: Minimum number of events in a bin. Bins with less events are discarded.
   :type min_n_events: int, optional

   :returns: Recession constant in days.
   :rtype: float

   .. rubric:: Notes

   Recession constant adapted after :cite:t:`kirchner_catchments_2009`, which is the
   decay constant of the exponential model fitted to the percentile-wise binned means
   of the recession segments. The higher the recession constant, the slower the head
   declines, and vice versa. The following function is fitted to the binned data
   (similar to the Exponential response function in Pastas):

   ..math::
       h(t) = - h_0 * (1 - exp(-t / c))

   where h(t) is the head at time t, h_0 is the final head as t goes to infinity, and
   c is the recession constant.















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