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ModularImageAnalysis (MIA) is an ImageJ plugin which provides a modular framework for assembling image and object analysis workflows. Detected objects can be transformed, filtered, measured and related. Analysis workflows are batch-enabled by default, allowing easy processing of high-content datasets.
package io.github.mianalysis.mia.process.analysis;
import java.util.TreeMap;
import org.apache.commons.math3.stat.regression.SimpleRegression;
import io.github.mianalysis.mia.process.math.CumStat;
/**
* Created by steph on 15/04/2017.
*/
public class MSDCalculator {
public static TreeMap calculate(TreeMap cumStats, int[] f, double[] x, double[] y, double[] z) {
for (int j = 0; j < f.length; j++) {//Incrementing over all frames with the possibility for that time step
for (int k = j; k < f.length; k++) {
int df = f[k]-f[j];
double dx = x[k] - x[j];
double dy = y[k] - y[j];
double dz = z[k] - z[j];
double val = dx * dx + dy * dy + dz * dz;
cumStats.putIfAbsent(df,new CumStat());
cumStats.get(df).addMeasure(val);
}
}
return cumStats;
}
public static TreeMap calculate(int[] f, double[] x, double[] y, double[] z) {
TreeMap cumStats = new TreeMap<>();
return calculate(cumStats,f,x,y,z);
}
public static double[] getLinearFit(TreeMap msd, int nPoints) {
int[] df = msd.keySet().stream().mapToInt(v->v).toArray();
double[] MSD = msd.values().stream().mapToDouble(CumStat::getMean).toArray();
return getLinearFit(df,MSD,nPoints);
}
public static double[] getLinearFit(int[] df, double[] msd, int nPoints) {
msd[0] = 0; //There should be no values with zero time-gap, so this would otherwise be NaN
if (df.length < nPoints) nPoints = df.length;
double[][] data = new double[nPoints][2];
for (int j=0;j