pastas.plotting.modelplots.Plotting.stacked_results
===================================================

.. py:method:: pastas.plotting.modelplots.Plotting.stacked_results(tmin: pandas.Timestamp | str | None = None, tmax: pandas.Timestamp | str | None = None, stackcolors: dict[str, str] | list[str] | None = None, stacklegend: bool = False, stacklegend_kws: dict | None = None, **kwargs) -> list[pastas.typing.Axes]

   
   Create a results plot, similar to `ml.plots.results()`, in which the
   individual contributions of stresses (in stressmodels with multiple stresses)
   are stacked.

   :param tmin: A string or pandas.Timestamp with the start date for the period
                (E.g. '1980-01-01 00:00:00'). Strings are converted to
                pandas.Timestamp internally.
   :type tmin: pandas.Timestamp or str, optional
   :param tmax: A string or pandas.Timestamp with the end date for the period
                (E.g. '2020-01-01 00:00:00'). Strings are converted to
                pandas.Timestamp internally.
   :type tmax: pandas.Timestamp or str, optional
   :param stackcolors: Either dictionary with stress names as keys and colors as values, or a
                       list of colors. By default None which applies colors according to the
                       order of stresses in the StressModel. Passing a dictionary can be useful
                       to apply the same color to each stress across multiple figures.
   :type stackcolors: dict or list, optional
   :param stacklegend: Add legend to the stacked plot.
   :type stacklegend: bool, optional
   :param stacklegend_kws: dict with keyword arguments for stackplot legend
   :type stacklegend_kws: dict, optional

   :returns: **axes**
   :rtype: list of axes objects















   ..
       !! processed by numpydoc !!