pastas.plotting.modelplots.Plotting

class pastas.plotting.modelplots.Plotting(ml: pastas.typing.Model)

Class that contains all plotting methods for Pastas models.

Pastas models come with a number of predefined plotting methods to quickly visualize a Model. All of these methods are contained in the plot attribute of a model. For example, if we stored a pastas.model.Model instance in the variable ml, the plot methods are available as follows:

ml.plots.results()

Methods

plot(→ pastas.typing.Axes)	Make a plot of the observed and simulated series.
results(→ dict[str, ...)	Plot the results of the model in a mosaic plot.
results_mosaic(→ dict[str, pastas.typing.Axes])	Plot the results of the model in a mosaic plot (deprecated).
decomposition(→ list[pastas.typing.Axes])	Plot the decomposition of a time-series in the different stresses.
diagnostics(→ pastas.typing.Axes)	Plot a window that helps in diagnosing basic model assumptions.
cum_frequency(→ pastas.typing.Axes)	Plot the cumulative frequency for the observations and simulation.
block_response(→ pastas.typing.Axes)	Plot the block response for a specific stressmodels.
step_response(→ pastas.typing.Axes)	Plot the step response for a specific stressmodels.
stresses(, **kwargs) → list[pastas.typing.Axes])	Create a graph with all the stresses used in the model.
contributions_pie(→ pastas.typing.Axes)	Make a pie chart of the contributions.
stacked_results(→ list[pastas.typing.Axes])	Create a results plot, similar to ml.plots.results().
series(→ pastas.typing.Axes)	Plot all the time series going into a Pastas Model.
summary(→ pastas.typing.Figure)	Create a plot with the results and diagnostics plot.
summary_pdf(→ pastas.typing.Figure)	Create a PDF file (A4) with the results and diagnostics plot.
pairplot(→ dict[str, pastas.typing.Axes])	Plot the correlation between all the time series going into a Pastas Model.
contribution(→ dict[str, pastas.typing.Axes])	Plot the contribution of a stressmodel and optionally the stress and the response.