pastas.plotting.modelplots.Plotting.decomposition
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.. py:method:: pastas.plotting.modelplots.Plotting.decomposition(tmin: pandas.Timestamp | str | None = None, tmax: pandas.Timestamp | str | None = None, ytick_base: bool = True, split: bool = True, figsize: tuple = (10, 8), axes: pastas.typing.Axes | None = None, name: str | None = None, return_warmup: bool = False, min_ylim_diff: float | None = None, **kwargs) -> pastas.typing.Axes

   
   Plot the decomposition of a time-series in the different stresses.

   :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 ytick_base: Make the ytick-base constant if True, set this base to float if a float.
   :type ytick_base: Boolean or float, optional
   :param split: Split the stresses in multiple stresses when possible. Default is True.
   :type split: bool, optional
   :param axes: Matplotlib Axes instance to plot the figure on to.
   :type axes: matplotlib.axes.Axes instance, optional
   :param figsize: tuple of size 2 to determine the figure size in inches.
   :type figsize: tuple, optional
   :param name: Name to give the simulated time series in the legend.
   :type name: str, optional
   :param return_warmup: Show the warmup-period. Default is false.
   :type return_warmup: bool, optional
   :param min_ylim_diff: Float with the difference in the ylimits. Default is None
   :type min_ylim_diff: float, optional
   :param \*\*kwargs: Optional arguments, passed on to the matplotlib.pyplot.subplots method.
   :type \*\*kwargs: dict, optional

   :returns: **axes**
   :rtype: list of matplotlib.axes.Axes















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