TimeDependent Class Referenceabstract

Time dependent objects. More...

Inheritance diagram for TimeDependent:

Public Member Functions

 TimeDependent (class Time *time)
 Construct. More...
virtual void proceedToNextState ()=0
 Proceed one time step. More...
virtual void prepareNextState ()=0
 Calculate next value (preparing next step). More...
virtual void init ()=0
 Reset all time dependent values.
virtual bool isNextStatePrepared ()=0
 Whether the next state is prepared or not.
virtual class TimegetTime () const
 Return pointer to the time object.

Detailed Description

Time dependent objects.

All objects which depend on time, especially on the size of the simulation time, must derive from this class.

Constructor & Destructor Documentation

TimeDependent::TimeDependent ( class Time time)


timeTime object on which this object is dependent If 0 is given here, a new time object with time step dt=1.0 will be created. In this case the object will be independent of all changes made to the time settings otherwise in the program. This must only be used for objects, which are not dependent on the current time settings, but inherit from this class anyway (some special forms of StochasticProcess variables, like Product).

Member Function Documentation

virtual void TimeDependent::prepareNextState ( )
pure virtual

Calculate next value (preparing next step).

This method should be overridden to implement the calculation of the next value, using information wich is available at the current time.

Implemented in NoiseSource, Regular, Poisson, MlNeuron, IfNeuron, SimpleSynapse, DifferentialEquation, ThetaNeuron, StochasticProcess, Noise, Wiener, EventMultiplexer, and Covariation.

virtual void TimeDependent::proceedToNextState ( )
pure virtual

Proceed one time step.

This method can be overridden to implement the proceeding of one time step. This makes new information available at the current time. (See also proceedToNextState()). The default just writes stochNextValue into stochCurrentValue.

Implemented in NoiseSource, MlNeuron, IfNeuron, SimpleSynapse, StochasticProcess, and Noise.