pastas.plotting.modelplots.Plotting.diagnostics
===============================================

.. py:method:: pastas.plotting.modelplots.Plotting.diagnostics(tmin: pandas.Timestamp | str | None = None, tmax: pandas.Timestamp | str | None = None, bins: int = 50, acf_options: dict | None = None, fig: pastas.typing.Figure | None = None, alpha: float = 0.05, **kwargs) -> pastas.typing.Axes

   
   Plot a window that helps in diagnosing basic model assumptions.

   :param tmin: start time for which to calculate the residuals.
   :type tmin: pandas.Timestamp or str, optional
   :param tmax: end time for which to calculate the residuals.
   :type tmax: pandas.Timestamp or str, optional
   :param bins: number of bins used for the histogram. 50 is default.
   :type bins: int optional
   :param acf_options: dictionary with keyword arguments that are passed on to pastas.stats.acf.
   :type acf_options: dict, optional
   :param fig: Optionally provide a matplotlib.pyplot.Figure instance to plot onto.
   :type fig: matplotlib.pyplot.Figure, optional
   :param alpha: Significance level to calculate the (1-alpha)-confidence intervals.
   :type alpha: float, optional
   :param \*\*kwargs: Optional keyword arguments, passed on to matplotlib.pyplot.figure method.
   :type \*\*kwargs: dict, optional

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

   .. rubric:: Examples

   >>> axes = ml.plots.diagnostics()

   .. rubric:: Notes

   This plot assumed 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.















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