Examples with FitSolver uk.ac.sussex.gdsc.smlm.data.config.FitProtos.FitSolver used on opensource projects

Example 1 with FitSolver

use of uk.ac.sussex.gdsc.smlm.data.config.FitProtos.FitSolver in project GDSC-SMLM by aherbert.

the class PeakFit method configureFitSolver.

/**
 * Show a dialog to configure the fit solver. The updated settings are saved to the settings file.
 * An error message is shown if the dialog is cancelled or the configuration is invalid.
 *
 * <p>The bounds are used to validate the camera model. The camera model must be large enough to
 * cover the source bounds. If larger then it will be cropped. Optionally an internal region of
 * the input image can be specified. This is relative to the width and height of the input image.
 * If no camera model is present then the bounds can be null.
 *
 * @param config the configuration
 * @param sourceBounds the source image bounds (used to validate the camera model dimensions)
 * @param bounds the crop bounds (relative to the input image, used to validate the camera model
 *        dimensions)
 * @param flags the flags
 * @return True if the configuration succeeded
 */
public static boolean configureFitSolver(FitEngineConfiguration config, Rectangle sourceBounds, Rectangle bounds, int flags) {
    final boolean extraOptions = BitFlagUtils.anySet(flags, FLAG_EXTRA_OPTIONS);
    final boolean ignoreCalibration = BitFlagUtils.anySet(flags, FLAG_IGNORE_CALIBRATION);
    final boolean saveSettings = BitFlagUtils.anyNotSet(flags, FLAG_NO_SAVE);
    final FitConfiguration fitConfig = config.getFitConfiguration();
    final CalibrationWriter calibration = fitConfig.getCalibrationWriter();
    final FitSolver fitSolver = fitConfig.getFitSolver();
    final boolean isLvm = fitSolver == FitSolver.LVM_LSE || fitSolver == FitSolver.LVM_WLSE || fitSolver == FitSolver.LVM_MLE;
    // Support the deprecated backtracking FastMLE solver as a plain FastMLE solver
    final boolean isFastMml = fitSolver == FitSolver.FAST_MLE || fitSolver == FitSolver.BACKTRACKING_FAST_MLE;
    final boolean isSteppingFunctionSolver = isLvm || isFastMml;
    if (fitSolver == FitSolver.MLE) {
        final ExtendedGenericDialog gd = new ExtendedGenericDialog(TITLE);
        if (!ignoreCalibration) {
            gd.addMessage("Maximum Likelihood Estimation requires CCD-type camera parameters");
            gd.addNumericField("Camera_bias", calibration.getBias(), 2, 6, "count");
            gd.addCheckbox("Model_camera_noise", fitConfig.isModelCamera());
            gd.addNumericField("Read_noise", calibration.getReadNoise(), 2, 6, "count");
            gd.addNumericField("Quantum_efficiency", calibration.getQuantumEfficiency(), 2, 6, "electron/photon");
            gd.addCheckbox("EM-CCD", calibration.isEmCcd());
        } else {
            gd.addMessage("Maximum Likelihood Estimation requires additional parameters");
        }
        final String[] searchNames = SettingsManager.getSearchMethodNames();
        gd.addChoice("Search_method", searchNames, FitProtosHelper.getName(fitConfig.getSearchMethod()));
        gd.addStringField("Relative_threshold", MathUtils.rounded(fitConfig.getRelativeThreshold()));
        gd.addStringField("Absolute_threshold", MathUtils.rounded(fitConfig.getAbsoluteThreshold()));
        gd.addNumericField("Max_iterations", fitConfig.getMaxIterations(), 0);
        gd.addNumericField("Max_function_evaluations", fitConfig.getMaxFunctionEvaluations(), 0);
        if (extraOptions) {
            gd.addCheckbox("Gradient_line_minimisation", fitConfig.isGradientLineMinimisation());
        }
        gd.showDialog();
        if (gd.wasCanceled()) {
            return false;
        }
        if (!ignoreCalibration) {
            calibration.setBias(Math.abs(gd.getNextNumber()));
            fitConfig.setModelCamera(gd.getNextBoolean());
            calibration.setReadNoise(Math.abs(gd.getNextNumber()));
            calibration.setQuantumEfficiency(Math.abs(gd.getNextNumber()));
            calibration.setCameraType((gd.getNextBoolean()) ? CameraType.EMCCD : CameraType.CCD);
            fitConfig.setCalibration(calibration.getCalibration());
        }
        fitConfig.setSearchMethod(SettingsManager.getSearchMethodValues()[gd.getNextChoiceIndex()]);
        fitConfig.setRelativeThreshold(getThresholdNumber(gd));
        fitConfig.setAbsoluteThreshold(getThresholdNumber(gd));
        fitConfig.setMaxIterations((int) gd.getNextNumber());
        fitConfig.setMaxFunctionEvaluations((int) gd.getNextNumber());
        if (extraOptions) {
            fitConfig.setGradientLineMinimisation(gd.getNextBoolean());
        } else {
            // This option is for the Conjugate Gradient optimiser and makes it less stable
            fitConfig.setGradientLineMinimisation(false);
        }
        if (saveSettings) {
            saveFitEngineSettings(config);
        }
        try {
            ParameterUtils.isAboveZero("Relative threshold", fitConfig.getRelativeThreshold());
            ParameterUtils.isAboveZero("Absolute threshold", fitConfig.getAbsoluteThreshold());
            ParameterUtils.isAboveZero("Max iterations", fitConfig.getMaxIterations());
            ParameterUtils.isAboveZero("Max function evaluations", fitConfig.getMaxFunctionEvaluations());
            fitConfig.getFunctionSolver();
        } catch (final IllegalArgumentException | IllegalStateException ex) {
            IJ.error(TITLE, ex.getMessage());
            return false;
        }
    } else if (isSteppingFunctionSolver) {
        final boolean requireCalibration = !ignoreCalibration && fitSolver != FitSolver.LVM_LSE;
        // Collect options for LVM fitting
        final ExtendedGenericDialog gd = new ExtendedGenericDialog(TITLE);
        final String fitSolverName = FitProtosHelper.getName(fitSolver);
        gd.addMessage(fitSolverName + " requires additional parameters");
        gd.addStringField("Relative_threshold", MathUtils.rounded(fitConfig.getRelativeThreshold()));
        gd.addStringField("Absolute_threshold", MathUtils.rounded(fitConfig.getAbsoluteThreshold()));
        gd.addStringField("Parameter_relative_threshold", MathUtils.rounded(fitConfig.getParameterRelativeThreshold()));
        gd.addStringField("Parameter_absolute_threshold", MathUtils.rounded(fitConfig.getParameterAbsoluteThreshold()));
        gd.addNumericField("Max_iterations", fitConfig.getMaxIterations(), 0);
        if (isLvm) {
            gd.addNumericField("Lambda", fitConfig.getLambda(), 4);
        }
        if (isFastMml) {
            gd.addCheckbox("Fixed_iterations", fitConfig.isFixedIterations());
            // This works because the proto configuration enum matches the named enum
            final String[] lineSearchNames = SettingsManager.getNames((Object[]) FastMleSteppingFunctionSolver.LineSearchMethod.values());
            gd.addChoice("Line_search_method", lineSearchNames, lineSearchNames[fitConfig.getLineSearchMethod().getNumber()]);
        }
        gd.addCheckbox("Use_clamping", fitConfig.isUseClamping());
        gd.addCheckbox("Dynamic_clamping", fitConfig.isUseDynamicClamping());
        final PSF psf = fitConfig.getPsf();
        final boolean isAstigmatism = psf.getPsfType() == PSFType.ASTIGMATIC_GAUSSIAN_2D;
        final int nParams = PsfHelper.getParameterCount(psf);
        if (extraOptions) {
            gd.addNumericField("Clamp_background", fitConfig.getClampBackground(), 2);
            gd.addNumericField("Clamp_signal", fitConfig.getClampSignal(), 2);
            gd.addNumericField("Clamp_x", fitConfig.getClampX(), 2);
            gd.addNumericField("Clamp_y", fitConfig.getClampY(), 2);
            if (isAstigmatism) {
                gd.addNumericField("Clamp_z", fitConfig.getClampZ(), 2);
            } else {
                if (nParams > 1 || !fitConfig.isFixedPsf()) {
                    gd.addNumericField("Clamp_sx", fitConfig.getClampXSd(), 2);
                }
                if (nParams > 1) {
                    gd.addNumericField("Clamp_sy", fitConfig.getClampYSd(), 2);
                }
                if (nParams > 2) {
                    gd.addNumericField("Clamp_angle", fitConfig.getClampAngle(), 2);
                }
            }
        }
        // Extra parameters are needed for calibrated fit solvers
        if (requireCalibration) {
            switch(calibration.getCameraType()) {
                case CCD:
                case EMCCD:
                case SCMOS:
                    break;
                default:
                    IJ.error(TITLE, fitSolverName + " requires camera calibration");
                    return false;
            }
            gd.addMessage(fitSolverName + " requires calibration for camera: " + CalibrationProtosHelper.getName(calibration.getCameraType()));
            if (calibration.isScmos()) {
                final String[] models = CameraModelManager.listCameraModels(true);
                gd.addChoice("Camera_model_name", models, fitConfig.getCameraModelName());
            } else {
                gd.addNumericField("Camera_bias", calibration.getBias(), 2, 6, "Count");
                gd.addNumericField("Gain", calibration.getCountPerPhoton(), 2, 6, "Count/photon");
                gd.addNumericField("Read_noise", calibration.getReadNoise(), 2, 6, "Count");
            }
        }
        gd.showDialog();
        if (gd.wasCanceled()) {
            return false;
        }
        fitConfig.setRelativeThreshold(getThresholdNumber(gd));
        fitConfig.setAbsoluteThreshold(getThresholdNumber(gd));
        fitConfig.setParameterRelativeThreshold(getThresholdNumber(gd));
        fitConfig.setParameterAbsoluteThreshold(getThresholdNumber(gd));
        fitConfig.setMaxIterations((int) gd.getNextNumber());
        if (isLvm) {
            fitConfig.setLambda(gd.getNextNumber());
        }
        if (isFastMml) {
            fitConfig.setFixedIterations(gd.getNextBoolean());
            fitConfig.setLineSearchMethod(gd.getNextChoiceIndex());
        }
        fitConfig.setUseClamping(gd.getNextBoolean());
        fitConfig.setUseDynamicClamping(gd.getNextBoolean());
        if (extraOptions) {
            fitConfig.setClampBackground(Math.abs(gd.getNextNumber()));
            fitConfig.setClampSignal(Math.abs(gd.getNextNumber()));
            fitConfig.setClampX(Math.abs(gd.getNextNumber()));
            fitConfig.setClampY(Math.abs(gd.getNextNumber()));
            if (isAstigmatism) {
                fitConfig.setClampZ(Math.abs(gd.getNextNumber()));
            } else {
                if (nParams > 1 || !fitConfig.isFixedPsf()) {
                    fitConfig.setClampXSd(Math.abs(gd.getNextNumber()));
                }
                if (nParams > 1) {
                    fitConfig.setClampYSd(Math.abs(gd.getNextNumber()));
                }
                if (nParams > 2) {
                    fitConfig.setClampAngle(Math.abs(gd.getNextNumber()));
                }
            }
        }
        if (requireCalibration) {
            if (calibration.isScmos()) {
                fitConfig.setCameraModelName(gd.getNextChoice());
            } else {
                calibration.setBias(Math.abs(gd.getNextNumber()));
                calibration.setCountPerPhoton(Math.abs(gd.getNextNumber()));
                calibration.setReadNoise(Math.abs(gd.getNextNumber()));
                fitConfig.setCalibration(calibration.getCalibration());
            }
        }
        // camera model is set.
        if (calibration.isScmos()) {
            fitConfig.setCameraModel(CameraModelManager.load(fitConfig.getCameraModelName()));
            if (!checkCameraModel(fitConfig, sourceBounds, bounds, true)) {
                return false;
            }
        }
        if (saveSettings) {
            saveFitEngineSettings(config);
        }
        try {
            if (isLvm) {
                ParameterUtils.isAboveZero("Lambda", fitConfig.getLambda());
            }
            // This call will check if the configuration is OK (including convergence criteria)
            fitConfig.getFunctionSolver();
        } catch (final IllegalArgumentException | IllegalStateException ex) {
            IJ.error(TITLE, ex.getMessage());
            return false;
        }
    } else {
        IJ.error(TITLE, "Unknown fit solver: " + fitSolver);
        return false;
    }
    if (config.isIncludeNeighbours() && !fitConfig.getFunctionSolver().isBounded()) {
        IJ.error(TITLE, "Including neighbours requires a bounded fit solver");
        return false;
    }
    return true;
}

Example 2 with FitSolver

use of uk.ac.sussex.gdsc.smlm.data.config.FitProtos.FitSolver in project GDSC-SMLM by aherbert.

the class PeakFit method getSolverName.

/**
 * Gets the solver name.
 *
 * @param fitConfig the fit config
 * @return the solver name
 */
public static String getSolverName(FitConfiguration fitConfig) {
    final FitSolver solver = fitConfig.getFitSolver();
    String name = FitProtosHelper.getName(solver);
    if (solver == FitSolver.MLE) {
        name += " " + FitProtosHelper.getName(fitConfig.getSearchMethod());
    }
    return name;
}