Dynamics for compartmented models¶
The SynchronousDynamics
and StochasticDynamics
classes define the basic mechanisms for the two main process
simulation approaches. In both cases, there is a common way to run
processes specified with compartmented models that massively simplify
the creation of simulations.
In practice, these classes typically require no subclassing, as all the
variant behaviour can be more effectively provided in the code for
the compartmented model of disease in a CompartmentedModel
subclass.
CompartmentedStochasticDynamics
¶

class
epydemic.
CompartmentedStochasticDynamics
(m, g=None)¶ Bases:
epydemic.stochasticdynamics.StochasticDynamics
A stochastic dynamics running a compartmented model. The behaviour of the simulation is completely described within the model rather than here.
Creating the dynamics involves providing the necessary model.

CompartmentedStochasticDynamics.
__init__
(m, g=None)¶ Create a dynamics over the given process model, optionally initialised to run on the given network.
Parameters:  m – the compartmented model for the disease process
 g – prototype network to run the dynamics over (optional)
To set up the experiment we provide the parameters for the experiment as usual.

CompartmentedStochasticDynamics.
setUp
(params)¶ Set up the experiment for a run. This performs the default action of copying the prototype network and then builds the model and uses it to initialise the nodes into the various compartments according to the parameters.
Params params: the experimental parameters
Running the model requires that we convert the event probability distribution into an event rate distribution.

CompartmentedStochasticDynamics.
eventRateDistribution
(t)¶ Convert the model’s event distribution into the ratebased distribution required by Gillespie simulation.
Parameters: t – the current time Returns: the event rate distribution
Finally, we defer the experimental results collection to the model.

CompartmentedStochasticDynamics.
experimentalResults
()¶ Report the model’s experimental results.
Returns: the results as seen by the model
CompartmentedSynchronousDynamics
¶

class
epydemic.
CompartmentedSynchronousDynamics
(m, g=None)¶ Bases:
epydemic.synchronousdynamics.SynchronousDynamics
A synchronous dynamics running a compartmented model. The behaviour of the simulation is completely described within the model rather than here.
Creating the dynamics involves providing the necessary model.

CompartmentedSynchronousDynamics.
__init__
(m, g=None)¶ Create a dynamicsover the given disease model, optionally initialised to run on the given prototype network.
Parameters:  m – the model
 g – prototype network to run over (optional)
To set up the experiment we provide the parameters for the experiment as usual.

CompartmentedSynchronousDynamics.
setUp
(params)¶ Set up the experiment for a run. This performs the default action of copying the prototype network and then builds the model and uses it to initialise the nodes into the various compartments according to the parameters.
Params params: the experimental parameters
Under synchronous dynamics we use the modelprovided event probabilities to test whether an event occurred at each possible locus in each discrete timestep.

CompartmentedSynchronousDynamics.
eventDistribution
(t)¶ Return the model’s event distribution.
Parameters: t – current time Returns: the event distribution
Finally, we defer the experimental results collection to the model.

CompartmentedSynchronousDynamics.
experimentalResults
()¶ Report the model’s experimental results.
Returns: the results as seen by the model