pastas.plotting.modelplots.Plotting.decomposition#
- pastas.plotting.modelplots.Plotting.decomposition(tmin: pandas.Timestamp | str | None = None, tmax: pandas.Timestamp | str | None = None, ytick_base: bool = True, split_contributions: bool = True, axes: pastas.typing.Axes | None = None, name: str | None = None, return_warmup: bool = False, min_ylim_diff: float | None = None, **kwargs) list[pastas.typing.Axes]#
Plot the decomposition of a time-series in the different stresses.
- Parameters:
tmin (pandas.Timestamp or str, optional) – 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.
tmax (pandas.Timestamp or str, optional) – 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.
ytick_base (Boolean or float, optional) – Make the ytick-base constant if True, set this base to float if a float.
split_contributions (bool, optional) – Split the stresses in multiple stresses when possible. Default is True.
axes (matplotlib.axes.Axes instance, optional) – Matplotlib Axes instance to plot the figure on to.
name (str, optional) – Name to give the simulated time series in the legend.
return_warmup (bool, optional) – Show the warmup-period. Default is false.
min_ylim_diff (float, optional) – Float with the difference in the ylimits. Default is None
**kwargs (dict, optional) – Optional arguments, passed on to the matplotlib.pyplot.subplots method.
- Returns:
axes
- Return type: