pastas.plotting.plots.diagnostics
=================================

.. py:function:: pastas.plotting.plots.diagnostics(series: pandas.Series, sim: pandas.Series | None = None, alpha: float = 0.05, bins: int = 50, acf_options: dict | None = None, figsize: tuple = (10, 5), fig: pastas.typing.Figure | None = None, heteroscedasicity: bool = True, **kwargs) -> pastas.typing.Axes

   
   Plot that helps in diagnosing basic model assumptions.

   :param series: Pandas Series with the residual time series to diagnose.
   :type series: pandas.Series
   :param sim: Pandas series with the simulated time series. Used to diagnose on
               heteroscedasticity. Ignored if heteroscedasticity is set to False.
   :type sim: pandas.Series, optional
   :param alpha: Significance level to calculate the (1-alpha)-confidence intervals.
   :type alpha: float, optional
   :param bins: Number of bins used for the histogram. 50 is default.
   :type bins: int optional
   :param acf_options: Dictionary with keyword arguments passed on to pastas.stats.acf.
   :type acf_options: dict, optional
   :param figsize: Tuple with the height and width of the figure in inches.
   :type figsize: tuple, optional
   :param fig: Optionally provide a Matplotib.Figure instance to plot onto.
   :type fig: Matplotib.Figure instance, optional
   :param heteroscedasicity: Create two additional subplots to check for heteroscedasticity. If true,
                             a simulated time series has to be provided with the sim argument.
   :type heteroscedasicity: bool, optional
   :param \*\*kwargs: Optional keyword arguments, passed on to plt.figure.
   :type \*\*kwargs: dict, optional

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

   .. rubric:: Examples

   >>> res = pd.Series(index=pd.date_range(start=0, periods=1000, freq="D"),
   >>>                 data=np.random.normal(0, 1, 1000))
   >>> ps.stats.plot_diagnostics(res)

   .. rubric:: Notes

   The two right-hand side plots assume that the noise or residuals follow a Normal
   distribution.

   .. seealso::

      :py:obj:`pastas.stats.acf`
          Method that computes the autocorrelation.

      :py:obj:`scipy.stats.probplot`
          Method use to plot the probability plot.















   ..
       !! processed by numpydoc !!