pastas.plotting.modelcompare.CompareModels.plot
===============================================

.. py:method:: pastas.plotting.modelcompare.CompareModels.plot(smdict: dict[int, list[str]] | None = None, normalized: bool = False, param_selection: list[str] | None = None, grid: bool = True, legend: bool = True, adjust_height: bool = False, legend_kwargs: dict[str, Any] | None = None, **fig_kwargs) -> None

   
   plot the models in a comparison plot.

   The resulting plot is similar to `ml.plots.results()`.

   :param smdict: dictionary with integers (index) as keys and list of stressmodel names as
                  values that have to be in each subplot. For example, `{0: ['prec',
                  'evap'], 1: ['rech']}` where stressmodels 'prec' and 'evap' are plotted
                  in the first contribution/response function window and 'rech' in the
                  second. By default, None, which creates a separate subplot for each
                  stressmodel.
   :type smdict: dict, optional
   :param normalized: normalize contributions such that minimum or maximum value is equal to
                      zero, by default False.
   :type normalized: bool, optional
   :param param_selection: list of (sub)strings of which parameters to show in table, by default None.
   :type param_selection: list, optional
   :param grid: grid in each subplot, by default True.
   :type grid: bool, optional
   :param legend: add legend in each subplot, by default True.
   :type legend: bool, optional
   :param adjust_height: adjust the height of the graphs, so that the vertical scale of all the
                         subplots on the left is equal. Default is False. When combining stress
                         contributions in one subplot, please also provide smdict for best results.
   :type adjust_height: bool, optional
   :param legend_kwargs: pass legend keyword arguments to plots.
   :type legend_kwargs: dict, optional
   :param \*\*fig_kwargs: pass keyword arguments to matplotlib.pyplot.subplot_mosaic.















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