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Running models : Working with visualisation tools : Plot value



Plot value against time
helper

This is the classic time-plot helper. It was not designed for
presentation in a printed document, but for giving the model developer a
robust, flexible display of how the value(s) for a single variable change
over time. It automatically re-scales, on both the X (time) and Y axes, it
can handle a variable no matter how deeply nested in multiple-instance
submodels, and it displays the results from previous runs for comparison.
It also gives the current values for all the variables in text form.

Each time you select a "Plot value against time" helper, you need to
choose the variable to be plotted before the helper is displayed. You are
alerted to this requirement by the message:

The following screen dumps show the standard ways this helper can be
used.

This shows the value for a single-valued variable "size" from three
successive runs of the model, using different parameter settings on each
run. Note the automatic use of a different colour for each run, and the
current value displayed at the bottom-left.

This shows the set of time plots for a single variable embedded inside a
fixed-membership multiple-instance submodel. Since the number of instances
is fixed for the duration of the run, we have the same number of lines
throughout. Note that the line for each instance is different, reflecting
the fact that each instance has different parameter values even though they
all behave according to the same equations.

Here again we have the plot for a single run, for a single variable
embedded inside a multiple-instance submodel. This time however the
submodel is a population submodel, with individual instances coming into
existence and disappearing during the course of the simulation run. Note
then how lines start during the simulation (unlike the previous example,
when all lines started at time zero), and also terminate during the course
of the simulation, as individual instances are killed off. Each line has a
different slope, reflecting the fact that the model was set up with a
randomly-determined parameter value for each instance.

In: Contents >> Running models >> Working with helpers