pastas.plotting.plots.acf
=========================

.. py:function:: pastas.plotting.plots.acf(series: pandas.Series, alpha: float = 0.05, lags: int = 365, acf_options: dict | None = None, smooth_conf: bool = True, color: str = 'k', ax: pastas.typing.Axes | None = None, figsize: tuple = (5, 2)) -> pastas.typing.Axes

   
   Plot of the autocorrelation function of a time series.

   :param series: Residual series to plot the autocorrelation function for.
   :type series: pandas.Series
   :param alpha: Significance level to calculate the (1-alpha)-confidence intervals. For 95%
                 confidence intervals, alpha should be 0.05.
   :type alpha: float, optional
   :param lags: Maximum number of lags (in days) to compute the autocorrelation for.
   :type lags: int, optional
   :param acf_options: Dictionary with keyword arguments passed on to pastas.stats.acf.
   :type acf_options: dict, optional
   :param smooth_conf: For irregular time series the confidence interval may be.
   :type smooth_conf: bool, optional
   :param color: Color of the vertical autocorrelation lines.
   :type color: str, optional
   :param ax: Matplotlib Axes instance to plot the ACF on. A new Figure and Axes is created
              when no value for ax is provided.
   :type ax: matplotlib.axes.Axes, optional
   :param figsize: 2-D Tuple to determine the size of the figure created. Ignored if ax is also
                   provided.
   :type figsize: tuple, optional

   :returns: **ax**
   :rtype: matplotlib.axes.Axes

   .. rubric:: Examples

   >>> res = pd.Series(index=pd.date_range(start=0, periods=1000, freq="D"),
   >>>                 data=np.random.rand(1000))
   >>> ps.plots.acf(res)

   :raises Warning if the ACF is empty. The plot will still be created to ensure that scripts:
   :raises will still run when dealing with many models.:















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