Plotting#

class Plotting(ml)[source]#

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.plot.results()
Parameters:

ml (Model) –

Methods#

__init__

block_response

Plot the block response for a specific stressmodels.

contributions_pie

Make a pie chart of the contributions.

cum_frequency

Plot the cumulative frequency for the observations and simulation.

decomposition

Plot the decomposition of a time-series in the different stresses.

diagnostics

Plot a window that helps in diagnosing basic model assumptions.

plot

Make a plot of the observed and simulated series.

results

Plot different results in one window to get a quick overview.

series

Method to plot all the time series going into a Pastas Model.

stacked_results

Create a results plot, similar to ml.plots.results(), in which the individual contributions of stresses (in stressmodels with multiple stresses) are stacked.

step_response

Plot the step response for a specific stressmodels.

stresses

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

summary_pdf

Create a PDF file (A4) with the results and diagnostics plot.