pastas.plotting.modelplots.Plotting.results#

pastas.plotting.modelplots.Plotting.results(tmin: pandas.Timestamp | str | None = None, tmax: pandas.Timestamp | str | None = None, split_contributions: bool = False, all_responses: bool = False, adjust_height: bool = True, return_warmup: bool = False, add_ylabels: bool = False, block_or_step: Literal['block', 'step'] = 'step', stderr: bool = False, return_dict: bool = False, **kwargs) → dict[str, pastas.typing.Axes] | list[pastas.typing.Axes]#

Plot the results of the model in a mosaic plot.

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.

  • split_contributions (bool, optional) – Split the contributions in multiple stresses when possible. Default is False.

  • all_responses (bool, optional) – Plot all responses if True. If False, only the first response per contribution is plotted. Default is False.

  • adjust_height (bool, optional) – Adjust the height of the graphs, so that the vertical scale of all the subplots on the left is equal. Default is True.

  • return_warmup (bool, optional) – Show, not return, the warmup-period. Default is False.

  • add_ylabels (bool, optional) – Add ylabels to the subplots. Default is False.

  • block_or_step ({"block", "step"}, optional) – Plot the block- or step-response on the right. Default is ‘step’.

  • stderr (bool, optional) – If True the standard error of the parameter values are shown.

  • return_dict (bool, optional) – If True, a dictionary with the axes is returned. If False, a list of axes is returned. Default is False.

  • **kwargs (dict, optional) – Optional arguments, passed on to the matplotlib.pyplot.figure method.

Return type:

Dictionary with the matplotlib.axes.Axes

Examples

>>> ml.plots.results_mosaic()