pastas.plotting.modelplots.Plotting.results
===========================================

.. py:method:: pastas.plotting.modelplots.Plotting.results(tmin: pandas.Timestamp | str | None = None, tmax: pandas.Timestamp | str | None = None, figsize: tuple = (10, 8), split: bool = False, adjust_height: bool = True, return_warmup: bool = False, block_or_step: str = 'step', stderr: bool = False, fig: pastas.typing.Figure | None = None, **kwargs) -> pastas.typing.Axes

   
   Plot different results in one window to get a quick overview.

   :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 figsize: tuple of size 2 to determine the figure size in inches.
   :type figsize: tuple, optional
   :param split: Split the stresses in multiple stresses when possible. Default is False.
   :type split: 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 True.
   :type adjust_height: bool, optional
   :param return_warmup: Show the warmup-period. Default is false.
   :type return_warmup: 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 stderr: If True the standard error of the parameter values are shown. Please be
                  aware of the conditions for reliable uncertainty estimates, more
                  information here:
                  https://pastas.readthedocs.io/stable/examples/diagnostic_checking.html
   :type stderr: bool, optional
   :param fig: Optionally provide a matplotib.Figure instance to plot onto.
   :type fig: matplotib.Figure instance, optional
   :param \*\*kwargs: Optional arguments, passed on to the matplotlib.pyplot.figure method.
   :type \*\*kwargs: dict, optional

   :rtype: list of matplotlib.axes.Axes

   .. rubric:: Examples

   >>> ml.plots.results()















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