NoiseGenerator.m

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namespace models{
namespace motoneuron{


/* (Autoinserted by mtoc++)
 * This source code has been filtered by the mtoc++ executable,
 * which generates code that can be processed by the doxygen documentation tool.
 *
 * On the other hand, it can neither be interpreted by MATLAB, nor can it be compiled with a C++ compiler.
 * Except for the comments, the function bodies of your M-file functions are untouched.
 * Consequently, the FILTER_SOURCE_FILES doxygen switch (default in our Doxyfile.template) will produce
 * attached source files that are highly readable by humans.
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 * Additionally, links in the doxygen generated documentation to the source code of functions and class members refer to
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 * However, the line numbers most likely do not correspond to the line numbers in the original MATLAB source files.
 */

class NoiseGenerator
  :public handle {
    public:

        RandSeed = 100000;


        logical DisableNoise = false;
    public:

        indepNoise;

        baseNoise;

        a;

        b;

        AP;

        factor;

        baseMean;

        indepMean;

        
    public:

        NoiseGenerator() {
            mc = metaclass(this);
            p = load(fullfile(fileparts(which(mc.Name))," neuro_input "));
            this.factor= sqrt(p.thetaP)*(1/p.scaleP);
            this.AP= p.AP;
            [this.b, this.a] = butter(2,p.LOWPASSP*2/1000," low ");
            /*  Base noise */
            this.baseNoise= p.noiseP;
            this.baseMean= mean(p.noiseP);
        }
        function  setFibreType(mu_fibretype) {
            rs = RandStream(" mt19937ar "," Seed ",round(this.RandSeed*mu_fibretype*100));
            noiseSI = filter(this.b,this.a,rs.randn(1,length(this.baseNoise)));
            /*  Independent noise */
            iNoise = this.factor/std(noiseSI)*noiseSI;
            /*  The mean current only goes into the independent noise, so we do this
             * transformation here already and use a 2D input function. */
            this.indepNoise= (iNoise + 1)*this.AP;
            this.indepMean= mean(this.indepNoise);
        }


        function u = getInput(double t) {

            if this.DisableNoise
                u = zeros(2,length(t));
                u(1,:) = this.baseMean;
                u(2,:) = this.indepMean;
            else
                pos = round(mod(t,length(this.baseNoise))+1);

                /*  total noise. the mean current incl. factor is build into the affine input
                 * mapping B. */
                u = [this.baseNoise(pos); this.indepNoise(pos)];
            end
        }
};
}
}