libpspm

libpspm is a C++ library to simulate Physiologically Structured Population Models (PSPMs).

It implements four numerical methods to discretize the McKendrick-von Foerster Partial Differential Equation (PDE), which forms the core of a PSPM, into a set of Ordinary Differential Equations (ODEs); see Zhang et al. 2017:

1. Fixed Mesh Upwind (FMU)
2. Implicit Fixed Mesh Upwind (IFMU)
3. Escalator Boxcar Train (EBT)
4. Characteristic Method (CM)

The resulting ODEs can be solved using different ODE-solvers. Two such are currently implemented:

1. Runge-Kutta 4-5 Cash-Karp
2. LSODA

libpspm supports multi-species systems with multiple size-structured and unstructured species.

Installation

To compile as a static library, just perform

make 
make check

This will create a static library (libpspm.a) in the lib folder. Link to this library as -lpspm while compiling your program.

Alternatively, just include the contents of the src and include folders in your respective project folders, compile each cpp file into an object file, and link with the objects of your project.

Stable release

Coming soon.

Development version

Latest development version can be found here: https://github.com/jaideep777/pspm/tree/develop

Usage

To solve a PSPM, you need to define two classes for the Environment and the Individual (not necessarily with those names). The Environment class must be derived from the EnvironmentBase class provided by the library. Although the Individual class can be defined as a standalone class, the easiest way to define it is also to derive it from the IndividualBase class provided by the library. In these derived classes you must implement the following member functions (one in Environment and four in Individual):

class Environment : public EnvironmentBase{
	public:
	// This function should perform environment computation, given the current state 
	void computeEnv(double t, Solver * S, std::vector<double>::iterator s, std::vector<double>::iterator dsdt){
	}
};

class Individual : public IndividualBase{
	public:
	// return the initial density (initial condition) as a function of 
	// the physiological variable x, environment _env, and input flux of newborns bf
	double init_density(double x, void * _env, double bf){
	}

	// return the growth rate as a function of 
	// the physiological variable x, time t, and environment _env
	double growthRate(double x, double t, void * _env){
	}

	// return the mortality rate as a function of 
	// the physiological variable x, time t, and environment _env
	double mortalityRate(double x, double t, void * _env){
	}

	// return the fecundity (birth rate) as a function of 
	// the physiological variable x, time t, and environment _env
	double birthRate(double x, double t, void * _env){
	}

};

Having defined these classes, create an Environment object and a species of Individuals as follows:

Environment E;
Species<Individual> spp;

Then create a Solver by specifying the PSPM method and the ODE solver, e.g.,

Solver S(SOLVER_FMU, "rk45ck");

Add the species and the environment to the solver:

// add the created Species 'spp' to the solver along with some species-specific properties 
S.addSpecies(25, xb, xm, false, &spp, 0, -1);
S.setEnvironment(&E);

Initialize and run the PSPM:

S.resetState();
S.initialize();
for (double t=0; t < 10; t=t+1) {
    S.step_to(t);
}

Concrete examples can be found in the demos folder. For more detailed tutorials explaining the demos, see:

1. Daphnia model -
2. RED model -

Author and contact

Jaideep Joshi [email protected]

Acknowledgement

This project was funded by a Marie Sklodowska-Curie fellowship H2020-MSCA-IF-2019, project PlantFATE.