pastas.plotting.modelplots.Plotting.plot#

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, figsize: tuple[float, float] | None = None, legend: bool = True, **kwargs) → pastas.typing.Axes#

Make a plot of the observed and simulated series.

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.

  • oseries (bool, optional) – True to plot the observed time series.

  • simulation (bool, optional) – True to plot the simulated time series.

  • ax (matplotlib.axes.Axes, optional) – Axes to add the plot to.

  • figsize (tuple, optional) – Tuple with the height and width of the figure in inches.

  • legend (bool, optional) – Boolean to determine to show the legend (True) or not (False).

Returns:

ax – matplotlib axes with the simulated and optionally the observed time series.

Return type:

matplotlib.axes.Axes

Examples

>>> ml.plot()