uk.ac.sussex.gdsc.smlm.function.gaussian.NsNbFixedGaussian2DFunction Maven / Gradle / Ivy
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/*-
* #%L
* Genome Damage and Stability Centre SMLM Package
*
* Software for single molecule localisation microscopy (SMLM)
* %%
* Copyright (C) 2011 - 2023 Alex Herbert
* %%
* This program 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.
*
* This program 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 this program. If not, see
* .
* #L%
*/
package uk.ac.sussex.gdsc.smlm.function.gaussian;
import uk.ac.sussex.gdsc.smlm.utils.StdMath;
/**
* Evaluates a 2-dimensional Gaussian function for a configured number of peaks.
*
* The single parameter x in the {@link #eval(int, double[])} function is assumed to be a linear
* index into 2-dimensional data. The dimensions of the data must be specified to allow unpacking to
* coordinates.
*
*
Data should be packed in descending dimension order, e.g. Y,X : Index for [x,y] = MaxX*y + x.
*/
public class NsNbFixedGaussian2DFunction extends MultiPeakGaussian2DFunction {
/** The number of gradient parameters for each Gaussian. */
protected static final int GRADIENT_PARAMETERS_PER_PEAK = 2;
/** The index for the The amplitude./height normalisation: 1/(2*pi*sx*sy). */
protected static final int N = 0;
/** The index for the The amplitude./height. */
protected static final int HEIGHT = 1;
/** The index for the x0 position pre-factor. */
protected static final int AA = 2;
/** The index for the x0 position gradient pre-factor. */
protected static final int AA2 = 3;
/** The pre-computed function factors for each Gaussian. */
protected final double[][] peakFactors;
/** The Gaussian parameters (a). */
protected double[] params;
/**
* Constructor.
*
* @param numberOfPeaks The number of peaks
* @param maxx The maximum x value of the 2-dimensional data (used to unpack a linear index into
* coordinates)
* @param maxy The maximum y value of the 2-dimensional data (used to unpack a linear index into
* coordinates)
*/
public NsNbFixedGaussian2DFunction(int numberOfPeaks, int maxx, int maxy) {
super(numberOfPeaks, maxx, maxy);
peakFactors = new double[numberOfPeaks][4];
}
@Override
public Gaussian2DFunction copy() {
return new NsNbFixedGaussian2DFunction(numberOfPeaks, maxx, maxy);
}
@Override
public void initialise(double[] a) {
this.params = a;
// Precalculate multiplication factors
for (int j = 0; j < numberOfPeaks; j++) {
final double sx = a[j * PARAMETERS_PER_PEAK + X_SD];
final double sx2 = sx * sx;
peakFactors[j][N] = ONE_OVER_TWO_PI / sx2;
peakFactors[j][HEIGHT] = a[j * PARAMETERS_PER_PEAK + SIGNAL] * peakFactors[j][N];
// All prefactors are negated since the Gaussian uses the exponential to the negative:
// (A/2*pi*sx*sy) * exp( -( a(x-x0)^2 + 2b(x-x0)(y-y0) + c(y-y0)^2 ) )
peakFactors[j][AA] = -0.5 / sx2;
peakFactors[j][AA2] = -2.0 * peakFactors[j][AA];
}
}
/**
* Evaluates a 2-dimensional fixed circular Gaussian function for multiple peaks.
*
*
{@inheritDoc}
*/
@Override
public double eval(final int x, final double[] dyda) {
// Track the position of the parameters
int apos = 0;
int dydapos = 0;
// First parameter is the background level
double y = params[BACKGROUND];
// Unpack the predictor into the dimensions
final int x1 = x / maxx;
final int x0 = x % maxx;
for (int j = 0; j < numberOfPeaks; j++) {
y += gaussian(x0, x1, dyda, apos, dydapos, peakFactors[j]);
apos += PARAMETERS_PER_PEAK;
dydapos += GRADIENT_PARAMETERS_PER_PEAK;
}
return y;
}
/**
* Evaluates a 2-dimensional fixed circular Gaussian function for multiple peaks.
*
*
{@inheritDoc}
*/
@Override
public double eval(final int x) {
// Track the position of the parameters
int apos = 0;
// First parameter is the background level
double y = params[BACKGROUND];
// Unpack the predictor into the dimensions
final int x1 = x / maxx;
final int x0 = x % maxx;
for (int j = 0; j < numberOfPeaks; j++, apos += PARAMETERS_PER_PEAK) {
y += gaussian(x0, x1, apos, peakFactors[j]);
}
return y;
}
/**
* Compute the Gaussian at a set offset from the centre.
*
* @param x0 the x0 offset
* @param x1 the x1 offset
* @param dyDa the first-order gradient
* @param apos the parameter position for the current peak
* @param dydapos the gradient position for the current peak
* @param factors the factors
* @return the Gaussian value
*/
protected double gaussian(final int x0, final int x1, final double[] dyDa, final int apos,
final int dydapos, final double[] factors) {
final double dx = x0 - params[apos + X_POSITION];
final double dy = x1 - params[apos + Y_POSITION];
// Calculate gradients
final double aadx2dy2 = factors[AA] * (dx * dx + dy * dy);
final double y = factors[HEIGHT] * StdMath.exp(aadx2dy2);
final double yaa2 = y * factors[AA2];
dyDa[dydapos] = yaa2 * dx;
dyDa[dydapos + 1] = yaa2 * dy;
return y;
}
/**
* Compute the Gaussian at a set offset from the centre.
*
* @param x0 the x0 offset
* @param x1 the x1 offset
* @param apos the parameter position for the current peak
* @param factors the factors
* @return the Gaussian value
*/
protected double gaussian(final int x0, final int x1, final int apos, final double[] factors) {
final double dx = x0 - params[apos + X_POSITION];
final double dy = x1 - params[apos + Y_POSITION];
return factors[HEIGHT] * StdMath.exp(factors[AA] * (dx * dx + dy * dy));
}
@Override
public boolean evaluatesBackground() {
return false;
}
@Override
public boolean evaluatesSignal() {
return false;
}
@Override
public boolean evaluatesAngle() {
return false;
}
@Override
public boolean evaluatesPosition() {
return true;
}
@Override
public boolean evaluatesSD0() {
return false;
}
@Override
public boolean evaluatesSD1() {
return false;
}
@Override
public int getGradientParametersPerPeak() {
return GRADIENT_PARAMETERS_PER_PEAK;
}
}