pastas.plotting.modelplots.Plotting.plot
========================================

.. py:method:: pastas.plotting.modelplots.Plotting.plot(tmin: pandas.Timestamp | str | None = None, tmax: pandas.Timestamp | str | None = None, oseries: bool = True, simulation: bool = True, ax: pastas.typing.Axes | None = None, legend: bool = True, **kwargs) -> pastas.typing.Axes

   
   Make a plot of the observed and simulated series.

   :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 oseries: True to plot the observed time series.
   :type oseries: bool, optional
   :param simulation: True to plot the simulated time series.
   :type simulation: bool, optional
   :param ax: Axes to add the plot to.
   :type ax: matplotlib.axes.Axes, optional
   :param legend: Boolean to determine to show the legend (True) or not (False).
   :type legend: bool, optional

   :returns: **ax** -- matplotlib axes with the simulated and optionally the observed time series.
   :rtype: matplotlib.axes.Axes

   .. rubric:: Examples

   >>> ml.plot()















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