uk.ac.sussex.gdsc.smlm.fitting.nonlinear.gradient.WPoissonGradientProcedure6 Maven / Gradle / Ivy

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Genome Damage and Stability Centre SMLM Package Software for single molecule localisation microscopy (SMLM)
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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.fitting.nonlinear.gradient;

import uk.ac.sussex.gdsc.core.utils.ValidationUtils;
import uk.ac.sussex.gdsc.smlm.function.Gradient1Function;

/**
 * Calculates the Fisher information matrix for a Poisson process.
 *
 * This procedure is based on computation of a modified Chi-squared expression to perform
 * Weighted Least Squares Estimation assuming a Poisson model with a Gaussian noise component. The
 * weight per observation is equal to 1/[variance + max(y, 0) + 1].
 *
 * See Lin, et al (2017) Algorithmic corrections for localization microscopy with sCMOS cameras -
 * characterisation of a computationally efficient localization approach. Optical Express 25, Issue
 * 10, pp 11701-11716.
 */
public class WPoissonGradientProcedure6 extends WPoissonGradientProcedure {

  /**
   * Instantiates a new procedure.
   *
   * @param y Data to fit
   * @param var the base variance of each observation (must be positive)
   * @param func Gradient function
   */
  public WPoissonGradientProcedure6(final double[] y, final double[] var,
      final Gradient1Function func) {
    super(y, var, func);
    ValidationUtils.checkArgument(numberOfGradients == 6, "Function must compute 6 gradients");
  }

  @Override
  public void execute(double value, double[] dyDa) {
    // Note: Ignore the value
    final double weight = this.wgt[yi++];
    data[0] += dyDa[0] * weight * dyDa[0];
    double dw;
    dw = dyDa[1] * weight;
    data[1] += dw * dyDa[0];
    data[2] += dw * dyDa[1];
    dw = dyDa[2] * weight;
    data[3] += dw * dyDa[0];
    data[4] += dw * dyDa[1];
    data[5] += dw * dyDa[2];
    dw = dyDa[3] * weight;
    data[6] += dw * dyDa[0];
    data[7] += dw * dyDa[1];
    data[8] += dw * dyDa[2];
    data[9] += dw * dyDa[3];
    dw = dyDa[4] * weight;
    data[10] += dw * dyDa[0];
    data[11] += dw * dyDa[1];
    data[12] += dw * dyDa[2];
    data[13] += dw * dyDa[3];
    data[14] += dw * dyDa[4];
    dw = dyDa[5] * weight;
    data[15] += dw * dyDa[0];
    data[16] += dw * dyDa[1];
    data[17] += dw * dyDa[2];
    data[18] += dw * dyDa[3];
    data[19] += dw * dyDa[4];
    data[20] += dw * dyDa[5];
  }

  @Override
  protected void initialiseWorkingMatrix() {
    GradientProcedureHelper.initialiseWorkingMatrix6(data);
  }

  @Override
  public void getMatrix(double[][] matrix) {
    GradientProcedureHelper.getMatrix6(data, matrix);
  }

  @Override
  public void getLinear(double[] matrix) {
    GradientProcedureHelper.getMatrix6(data, matrix);
  }
}