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])

Deprecated method to plot the results of the model in a mosaic plot.

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])

This method creates a graph with all the stresses used in the model.

contributions_pie(→ pastas.typing.Axes)

Make a pie chart of the contributions. This plot is based on the TNO

stacked_results(→ list[pastas.typing.Axes])

Create a results plot, similar to ml.plots.results(), in which the

series(→ pastas.typing.Axes)

Method to 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])

Method to plot the correlation between all the time series going

contribution(→ dict[str, pastas.typing.Axes])

Plot the contribution of a stressmodel and optionally the stress and the response.