An abstract class with some basic functionality for Matplotlib visualizations, such as keypress handling and axes instantiation. This class is subclassed by built-in visualizations TimeSeries
and Charts
, and can also be subclassed by user visualizations. It is itself a subclass of BaseVisualization
. It should not be instantiated directly and cannot be used directly as a visualization class.
Initialization Parameters
model — Helipad, required
The model object.
Methods
Click a method name for more detailed documentation.
addKeypress( key, fn )
Registers a function to be run when a key is pressed in a Matplotlib visualizer.
fn
will run ifkey
is pressed at any time when the plot window is in focus. To narrow the focus to a particular plot, definecatchKeypress()
in a subclass ofChartPlot
.onStart( )
Called when a model begins or resumes after pausing. This function may be overridden by any subclass of
BaseVisualization
, but should not be called from model code.onStop( )
Called when a model pauses or terminates. This function may be overridden by any subclass of
BaseVisualization
, but should not be called from model code.pick( axes, event )
Receives any pick events not routed to individual plot objects. This function may be overridden by any subclass of
MPLVisualization
for visualizer-specific events, but should not be called from model code.event( t, color, **kwargs )
Called when an event is triggered, in order to be reflected in the visualization. For example, a line is drawn in
TimeSeries
, and the background flashes inCharts
. Any kwargs passed toEvents.add()
are passed to this function, allowing subclasses to receive custom arguments (for example,linestyle
inTimeSeries.event()
). This method is mandatory for subclasses to implement.This function is called automatically when an event is triggered and should not be called by user code.
launch( title )
Launches the visualization window (if in Tkinter) or cell (if in Jupyter). This method is required to be implemented by subclasses, which should call
super().launch(title)
after creating the Matplotlib figure object.refresh( data )
Updates the visualization with new data and refreshes the display. This is mandatory for any subclasses to implement. Subclasses implementing this method should also use it to store any data necessary for future visualizer operations.
This function should only rarely be called by the user; call
model.step()
instead to increment the model by one period and update the graph.terminate( )
Cleanup on model termination. This function is called automatically from
model.terminate()
and should not be called from user code.
Object Properties
isNull — bool
True
when the model should run as-if with no visualization, for example if all the plots are unselected.False
indicates the window can be launched.lastUpdate — int
Because the model can be set to refresh the visualization only every so many periods, this property records the model time when data was last drawn to the visualization. This property is
None
until the visualization window is launched.Initial value: None
fig — matplotlib.Figure
The Matplotlib Figure object used for rendering the visualization as a whole.
plots — dict{str:Plot}
A dict of registered
TimeSeriesPlot
objects.activePlots — dict{str:Plot}
The subset of plots containing the
Plot
objects that are currently selected.keyListeners — dict{str: list[func]}
Where functions defined in
MPLVisualization.addKeypress()
are stored.Initial value: {}
dim — tuple(int, int)
The height and width at which the visualization window should open at launch, if using the Tkinter frontend. Default is the screen height and 2/3 the screen width.
Initial value: None
pos — tuple(int, int)
The
(x, y)
position at which the visualization window should open at launch, if using the Tkinter frontend. The defaultx
position places the window immediately to the right of the control panel.Initial value: (400, 0)
Hooks
Click a hook name for more detailed documentation.
visualRefresh( model, visual )
Runs when the visualizer updates, every n periods as determined by the
refresh
parameter.visualLaunch( model, visual )
Runs when
model.launchVisual()
is called, after the visualization is launched. Use themodelPostSetup
hook to catch before the visualization is launched.
charwick
Jun 27, 2022 at 22:16Visualization can be the slowest part of running a model. Matplotlib can generate smaller figures much more quickly, so you may wish to launch with a smaller window. This code (which works for any subclass of
MPLVisualization
) initializes the visualization window at 1000×800.