pastas.plotting.modelplots.Plotting.results_mosaic
==================================================

.. py:method:: pastas.plotting.modelplots.Plotting.results_mosaic(tmin: pandas.Timestamp | str | None = None, tmax: pandas.Timestamp | str | None = None, stderr: bool = False, block_or_step: str = 'step', return_warmup: bool = False, adjust_height: bool = True, figsize: tuple[float, float] | None = None, layout: Literal['constrained', 'tight', 'compressed', 'none'] | None = 'constrained', fig_kwargs: dict[str, Any] | None = None) -> dict[str, pastas.typing.Axes]

   
   Plot the results of the model in a mosaic plot.

   :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 stderr: If True the standard error of the parameter values are shown.
   :type stderr: bool, optional
   :param block_or_step: Plot the block- or step-response on the right. Default is 'step'.
   :type block_or_step: str, 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 True.
   :type adjust_height: bool, optional
   :param return_warmup: Show the warmup-period. Default is False.
   :type return_warmup: bool, optional
   :param figsize: tuple of size 2 to determine the figure size in inches.
   :type figsize: tuple, optional
   :param \*\*kwargs: Optional arguments, passed on to the matplotlib.pyplot.figure method.
   :type \*\*kwargs: dict, optional

   :rtype: Dictionary with the matplotlib.axes.Axes

   .. rubric:: Examples

   >>> ml.plots.results_mosaic()















   ..
       !! processed by numpydoc !!