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Simulation.java
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Sun, May 12, 05:51

Simulation.java
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/*
* DeconvolutionLab2
*
* Conditions of use: You are free to use this software for research or
* educational purposes. In addition, we expect you to include adequate
* citations and acknowledgments whenever you present or publish results that
* are based on it.
*
* Reference: DeconvolutionLab2: An Open-Source Software for Deconvolution
* Microscopy D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz,
* R. Guiet, C. Vonesch, M Unser, Methods of Elsevier, 2017.
*/
/*
* Copyright 2010-2017 Biomedical Imaging Group at the EPFL.
*
* This file is part of DeconvolutionLab2 (DL2).
*
* DL2 is free software: you can redistribute it and/or modify it under the
* terms of the GNU General Public License as published by the Free Software
* Foundation, either version 3 of the License, or (at your option) any later
* version.
*
* DL2 is distributed in the hope that it will be useful, but WITHOUT ANY
* WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
* A PARTICULAR PURPOSE. See the GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License along with
* DL2. If not, see <http://www.gnu.org/licenses/>.
*/
package deconvolution.algorithm;
import java.util.concurrent.Callable;
import bilib.tools.PsRandom;
import signal.ComplexSignal;
import signal.Operations;
import signal.RealSignal;
import signal.SignalCollector;
public class Simulation extends AbstractAlgorithm implements Callable<RealSignal> {
private static PsRandom rand = new PsRandom(1234);
private double mean = 0.0;
private double stdev = 10.0;
private double poisson = 0.0;
public Simulation(double mean, double stdev, double poisson) {
super();
this.mean = mean;
this.stdev = stdev;
this.poisson = poisson;
}
@Override
public RealSignal call() {
ComplexSignal Y = fft.transform(y);
ComplexSignal H = fft.transform(h);
ComplexSignal X = Operations.multiply(H, Y);
SignalCollector.free(Y);
SignalCollector.free(H);
RealSignal x = fft.inverse(X);
SignalCollector.free(X);
gaussian(x, mean, stdev);
poisson(x, poisson);
return x;
}
public void gaussian(RealSignal x, double mean, double sd) {
for (int k = 0; k < x.nz; k++) {
float[] slice = x.getXY(k);
for (int j = 0; j < x.ny * x.nx; j++) {
double a = slice[j];
slice[j] += (float) rand.nextGaussian(mean, sd);
}
}
}
public void poisson(RealSignal x, double factor) {
if (factor < Operations.epsilon)
return;
double f = 1.0/(factor);
for (int k = 0; k < x.nz; k++) {
float[] slice = x.getXY(k);
for (int j = 0; j < x.ny * x.nx; j++)
if (slice[j] > Operations.epsilon) {
slice[j] = (float)(rand.nextPoissonian(f*(slice[j])) * factor);
}
}
}
@Override
public String getName() {
return "Simulation with noise";
}
@Override
public String[] getShortnames() {
return new String[] {"SIM", "SIMU"};
}
@Override
public int getComplexityNumberofFFT() {
return 3;
}
@Override
public double getMemoryFootprintRatio() {
return 8.0;
}
@Override
public boolean isRegularized() {
return false;
}
@Override
public boolean isStepControllable() {
return false;
}
@Override
public boolean isIterative() {
return false;
}
@Override
public boolean isWaveletsBased() {
return false;
}
@Override
public AbstractAlgorithm setParameters(double... params) {
if (params == null)
return this;
if (params.length > 0)
mean = params[0];
if (params.length > 1)
stdev = params[1];
if (params.length > 2)
poisson = params[2];
return this;
}
@Override
public double[] getDefaultParameters() {
return new double[] {0, 1, 0};
}
@Override
public double[] getParameters() {
return new double[] {mean, stdev, poisson};
}
@Override
public double getRegularizationFactor() {
return 0.0;
}
@Override
public double getStepFactor() {
return 0;
}
}

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