Plotting

class Plotting(ml)

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()

Parameters

ml (Model) –

Methods

__init__
block_response	Plot the block response for a specific stressmodels.
contribution	Plot the contribution of a stressmodel and optionally the stress and the response.
contributions_pie
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.
pairplot	Method to plot all the time series going into a Pastas Model. :param tmin: :type tmin: str or pd.Timestamp :param tmax: :type tmax: str or pd.Timestamp :param bins: Number of bins in the histogram, by default None which uses Sturge's Rule to determine the number bins :type bins: Optional[int], optional :param split: Split the stresses in multiple stresses when possible. :type split: bool, optional.
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	Create a plot with the results and diagnostics plot.
summary_pdf	Create a PDF file (A4) with the results and diagnostics plot.