Examples with OptModel org.vcell.vmicro.workflow.data.OptModel used on opensource projects

Example 1 with OptModel

use of org.vcell.vmicro.workflow.data.OptModel in project vcell by virtualcell.

the class GenerateKenworthyOptModel method compute0.

@Override
protected void compute0(TaskContext context, final ClientTaskStatusSupport clientTaskStatusSupport) throws Exception {
    double bleachRadiusValue = context.getData(bleachRadius);
    ModelType modelType = ModelType.valueOf(context.getData(this.modelType));
    OptModel optModel = null;
    switch(modelType) {
        case KenworthyUniformDisk2Param:
            {
                optModel = new OptModelKenworthyUniformDisk2P(bleachRadiusValue);
                break;
            }
        case KenworthyUniformDisk3Param:
            {
                optModel = new OptModelKenworthyUniformDisk3P(bleachRadiusValue);
                break;
            }
        case KenworthyEffectiveRadius:
            {
                throw new RuntimeException("not yet implemented");
            // optModel = new OptModelKenworthyEffectiveRadius(bleachRadiusValue, detectionRadiusValue);
            // break;
            }
        default:
            {
                throw new RuntimeException("model type " + modelType + " not supported");
            }
    }
    context.setData(this.optModel, optModel);
}

Example 2 with OptModel

use of org.vcell.vmicro.workflow.data.OptModel in project vcell by virtualcell.

the class Generate2DOptContext method compute0.

@Override
protected void compute0(TaskContext context, final ClientTaskStatusSupport clientTaskStatusSupport) throws ExpressionException {
    // get inputs
    RowColumnResultSet normExpDataset = context.getData(normExpData);
    RowColumnResultSet measurementErrorDataset = context.getData(normalizedMeasurementErrors);
    OptModel optmodel = context.getData(optModel);
    // do op
    Generate2DOptContextOp op = new Generate2DOptContextOp();
    ErrorFunction errorFunction = context.getData(this.errorFunction);
    OptContext optcontext = op.generate2DOptContext(optmodel, normExpDataset, measurementErrorDataset, errorFunction);
    // set output
    context.setData(optContext, optcontext);
}

Example 3 with OptModel

use of org.vcell.vmicro.workflow.data.OptModel in project vcell by virtualcell.

the class PhotoactivationExperimentTest method analyzePhotoactivation.

/**
 * Fits raw image time series data to uniform disk models (with Guassian or Uniform fluorescence).
 *
 * @param rawTimeSeriesImages
 * @param localWorkspace
 * @throws Exception
 */
private static void analyzePhotoactivation(ImageTimeSeries<UShortImage> rawTimeSeriesImages, LocalWorkspace localWorkspace) throws Exception {
    // 
    // correct the timestamps (1 per second).
    // 
    double[] timeStamps = rawTimeSeriesImages.getImageTimeStamps();
    for (int i = 0; i < timeStamps.length; i++) {
        timeStamps[i] = i;
    }
    new DisplayTimeSeriesOp().displayImageTimeSeries(rawTimeSeriesImages, "raw images", (WindowListener) null);
    ImageTimeSeries<UShortImage> blurredRaw = blurTimeSeries(rawTimeSeriesImages);
    new DisplayTimeSeriesOp().displayImageTimeSeries(blurredRaw, "blurred raw images", (WindowListener) null);
    double cellThreshold = 0.4;
    GeometryRoisAndActivationTiming cellROIresults = new GenerateCellROIsFromRawPhotoactivationTimeSeriesOp().generate(blurredRaw, cellThreshold);
    ROI backgroundROI = cellROIresults.backgroundROI_2D;
    ROI cellROI = cellROIresults.cellROI_2D;
    int indexOfFirstPostactivation = cellROIresults.indexOfFirstPostactivation;
    boolean backgroundSubtract = false;
    boolean normalizeByPreActivation = false;
    NormalizedPhotoactivationDataResults normResults = new GenerateNormalizedPhotoactivationDataOp().generate(rawTimeSeriesImages, backgroundROI, indexOfFirstPostactivation, backgroundSubtract, normalizeByPreActivation);
    ImageTimeSeries<FloatImage> normalizedTimeSeries = normResults.normalizedPhotoactivationData;
    FloatImage preactivationAvg = normResults.preactivationAverageImage;
    FloatImage normalizedPostactivation = normalizedTimeSeries.getAllImages()[0];
    new DisplayTimeSeriesOp().displayImageTimeSeries(normalizedTimeSeries, "normalized images", (WindowListener) null);
    // 
    // create a single bleach ROI by thresholding
    // 
    double activatedThreshold = 1000;
    ROI activatedROI = new GenerateActivationRoiOp().generateActivatedRoi(blurredRaw.getAllImages()[indexOfFirstPostactivation], cellROI, activatedThreshold);
    new DisplayImageOp().displayImage(activatedROI.getRoiImages()[0], "activated roi", null);
    new DisplayImageOp().displayImage(cellROI.getRoiImages()[0], "cell roi", null);
    new DisplayImageOp().displayImage(backgroundROI.getRoiImages()[0], "background roi", null);
    {
        // 
        // only use bleach ROI for fitting etc.
        // 
        NormalizedSampleFunction[] dataROIs = new NormalizedSampleFunction[] { NormalizedSampleFunction.fromROI(activatedROI) };
        // 
        // get reduced data and errors for each ROI
        // 
        RowColumnResultSet reducedData = new GenerateReducedDataOp().generateReducedData(normalizedTimeSeries, dataROIs);
        RowColumnResultSet measurementErrors = new ComputeMeasurementErrorOp().computeNormalizedMeasurementError(dataROIs, indexOfFirstPostactivation, rawTimeSeriesImages, preactivationAvg, null);
        DisplayPlotOp displayReducedData = new DisplayPlotOp();
        displayReducedData.displayPlot(reducedData, "reduced data", null);
        DisplayPlotOp displayMeasurementError = new DisplayPlotOp();
        displayMeasurementError.displayPlot(measurementErrors, "measurement error", null);
        // 
        // 2 parameter uniform disk model
        // 
        Parameter tau = new Parameter("tau", 0.001, 200.0, 1.0, 0.1);
        Parameter f_init = new Parameter("f_init", 0.5, 4000, 1.0, 1.0);
        Parameter f_final = new Parameter("f_final", 0.01, 4000, 1.0, 0.5);
        Parameter[] parameters = new Parameter[] { tau, f_init, f_final };
        OptModel optModel = new OptModel("photoactivation (activated roi)", parameters) {

            @Override
            public double[][] getSolution0(double[] newParams, double[] solutionTimePoints) {
                double tau = newParams[0];
                double max = newParams[1];
                double offset = newParams[2];
                double[][] solution = new double[1][solutionTimePoints.length];
                for (int i = 0; i < solution[0].length; i++) {
                    double t = solutionTimePoints[i];
                    solution[0][i] = offset + (max - offset) * Math.exp(-t / tau);
                }
                return solution;
            }

            @Override
            public double getPenalty(double[] parameters2) {
                return 0;
            }
        };
        ErrorFunction errorFunction = new ErrorFunctionNoiseWeightedL2();
        OptContext uniformDisk2Context = new Generate2DOptContextOp().generate2DOptContext(optModel, reducedData, measurementErrors, errorFunction);
        new DisplayInteractiveModelOp().displayOptModel(uniformDisk2Context, dataROIs, localWorkspace, "nonspatial photoactivation - activated ROI only", null);
    }
    {
        // 
        // only activation ROI for chemistry, cellROI for bleaching
        // 
        NormalizedSampleFunction[] dataROIs = new NormalizedSampleFunction[] { NormalizedSampleFunction.fromROI(activatedROI), NormalizedSampleFunction.fromROI(cellROI) };
        // 
        // get reduced data and errors for each ROI
        // 
        RowColumnResultSet reducedData = new GenerateReducedDataOp().generateReducedData(normalizedTimeSeries, dataROIs);
        RowColumnResultSet measurementErrors = new ComputeMeasurementErrorOp().computeNormalizedMeasurementError(dataROIs, indexOfFirstPostactivation, rawTimeSeriesImages, preactivationAvg, null);
        DisplayPlotOp displayReducedData = new DisplayPlotOp();
        displayReducedData.displayPlot(reducedData, "reduced data (2)", null);
        DisplayPlotOp displayMeasurementError = new DisplayPlotOp();
        displayMeasurementError.displayPlot(measurementErrors, "measurement error (2)", null);
        // 
        // 2 parameter uniform disk model
        // 
        Parameter tau_active = new Parameter("tau_active", 0.001, 200.0, 1.0, 0.1);
        Parameter f_active_init = new Parameter("f_active_init", 0.5, 4000, 1.0, 1.0);
        Parameter f_active_amplitude = new Parameter("f_active_amplitude", 0.01, 4000, 1.0, 0.5);
        Parameter f_cell_init = new Parameter("f_cell_init", 0.01, 4000, 1.0, 0.1);
        Parameter f_cell_amplitude = new Parameter("f_cell_amplitude", 0.01, 4000, 1.0, 0.1);
        Parameter tau_cell = new Parameter("tau_cell", 0.00001, 200, 1.0, 1);
        Parameter[] parameters = new Parameter[] { tau_active, f_active_init, f_active_amplitude, tau_cell, f_cell_init, f_cell_amplitude };
        OptModel optModel = new OptModel("photoactivation (activated and cell rois)", parameters) {

            @Override
            public double[][] getSolution0(double[] newParams, double[] solutionTimePoints) {
                double tau_active = newParams[0];
                double max_active = newParams[1];
                double amplitude_active = newParams[2];
                double tau_cell = newParams[3];
                double max_cell = newParams[4];
                double amplitude_cell = newParams[5];
                final int ACTIVE_ROI = 0;
                final int CELL_ROI = 1;
                final int NUM_ROIS = 2;
                double[][] solution = new double[NUM_ROIS][solutionTimePoints.length];
                for (int i = 0; i < solution[0].length; i++) {
                    double t = solutionTimePoints[i];
                    solution[ACTIVE_ROI][i] = (max_active - amplitude_active) + (amplitude_active) * Math.exp(-t / tau_active) * Math.exp(-t / tau_cell);
                    solution[CELL_ROI][i] = (max_cell - amplitude_cell) + (amplitude_cell) * Math.exp(-t / tau_cell);
                }
                return solution;
            }

            @Override
            public double getPenalty(double[] parameters2) {
                return 0;
            }
        };
        ErrorFunctionNoiseWeightedL2 errorFunction = new ErrorFunctionNoiseWeightedL2();
        OptContext uniformDisk2Context = new Generate2DOptContextOp().generate2DOptContext(optModel, reducedData, measurementErrors, errorFunction);
        new DisplayInteractiveModelOp().displayOptModel(uniformDisk2Context, dataROIs, localWorkspace, "nonspatial photoactivation - activated and cell ROIs", null);
    }
}

Example 4 with OptModel

use of org.vcell.vmicro.workflow.data.OptModel in project vcell by virtualcell.

the class GenerateRefSimOptModel method compute0.

@Override
protected void compute0(TaskContext context, final ClientTaskStatusSupport clientTaskStatusSupport) throws Exception {
    RowColumnResultSet refSimDataset = context.getData(refSimData);
    double[] refSimTimePoints = refSimDataset.extractColumn(0);
    int numRois = refSimDataset.getDataColumnCount() - 1;
    int numRefSimTimes = refSimDataset.getRowCount();
    double[][] refSimData = new double[numRois][numRefSimTimes];
    for (int roi = 0; roi < numRois; roi++) {
        double[] roiData = refSimDataset.extractColumn(roi + 1);
        for (int t = 0; t < numRefSimTimes; t++) {
            refSimData[roi][t] = roiData[t];
        }
    }
    ModelType modelType = ModelType.valueOf(context.getData(this.modelType));
    OptModel optModel = null;
    Double refSimDiff = context.getData(refSimDiffusionRate);
    switch(modelType) {
        case DiffOne:
            {
                optModel = new OptModelOneDiff(refSimData, refSimTimePoints, refSimDiff);
                break;
            }
        case DiffTwoWithoutPenalty:
            {
                optModel = new OptModelTwoDiffWithoutPenalty(refSimData, refSimTimePoints, refSimDiff);
                break;
            }
        case DiffTwoWithPenalty:
            {
                optModel = new OptModelTwoDiffWithPenalty(refSimData, refSimTimePoints, refSimDiff);
                break;
            }
        default:
            {
                throw new RuntimeException("model type " + modelType + " not supported");
            }
    }
    context.setData(this.optModel, optModel);
}

Example 5 with OptModel

use of org.vcell.vmicro.workflow.data.OptModel in project vcell by virtualcell.

the class KenworthyWorkflowTest method analyzeKeyworthy.

/**
 * Fits raw image time series data to uniform disk models (with Guassian or Uniform fluorescence).
 *
 * @param rawTimeSeriesImages
 * @param localWorkspace
 * @throws Exception
 */
private static void analyzeKeyworthy(ImageTimeSeries<UShortImage> rawTimeSeriesImages, LocalWorkspace localWorkspace) throws Exception {
    new DisplayTimeSeriesOp().displayImageTimeSeries(rawTimeSeriesImages, "raw images", (WindowListener) null);
    double cellThreshold = 0.5;
    GeometryRoisAndBleachTiming cellROIresults = new GenerateCellROIsFromRawFrapTimeSeriesOp().generate(rawTimeSeriesImages, cellThreshold);
    ROI backgroundROI = cellROIresults.backgroundROI_2D;
    ROI cellROI = cellROIresults.cellROI_2D;
    int indexOfFirstPostbleach = cellROIresults.indexOfFirstPostbleach;
    new DisplayImageOp().displayImage(backgroundROI.getRoiImages()[0], "background ROI", null);
    new DisplayImageOp().displayImage(cellROI.getRoiImages()[0], "cell ROI", null);
    NormalizedFrapDataResults normResults = new GenerateNormalizedFrapDataOp().generate(rawTimeSeriesImages, backgroundROI, indexOfFirstPostbleach);
    ImageTimeSeries<FloatImage> normalizedTimeSeries = normResults.normalizedFrapData;
    FloatImage prebleachAvg = normResults.prebleachAverage;
    FloatImage normalizedPostbleach = normalizedTimeSeries.getAllImages()[0];
    new DisplayTimeSeriesOp().displayImageTimeSeries(normalizedTimeSeries, "normalized images", (WindowListener) null);
    // 
    // create a single bleach ROI by thresholding
    // 
    double bleachThreshold = 0.80;
    ROI bleachROI = new GenerateBleachRoiOp().generateBleachRoi(normalizedPostbleach, cellROI, bleachThreshold);
    // 
    // only use bleach ROI for fitting etc.
    // 
    // ROI[] dataROIs = new ROI[] { bleachROI };
    // 
    // fit 2D Gaussian to normalized data to determine center, radius and K factor of bleach (assuming exp(-exp
    // 
    FitBleachSpotOpResults fitSpotResults = new FitBleachSpotOp().fit(NormalizedSampleFunction.fromROI(bleachROI), normalizedTimeSeries.getAllImages()[0]);
    double bleachFactorK_GaussianFit = fitSpotResults.bleachFactorK_GaussianFit;
    double bleachRadius_GaussianFit = fitSpotResults.bleachRadius_GaussianFit;
    double bleachRadius_ROI = fitSpotResults.bleachRadius_ROI;
    double centerX_GaussianFit = fitSpotResults.centerX_GaussianFit;
    double centerX_ROI = fitSpotResults.centerX_ROI;
    double centerY_GaussianFit = fitSpotResults.centerY_GaussianFit;
    double centerY_ROI = fitSpotResults.centerY_ROI;
    NormalizedSampleFunction[] sampleFunctions = new NormalizedSampleFunction[] { NormalizedSampleFunction.fromROI(bleachROI) };
    // 
    // get reduced data and errors for each ROI
    // 
    RowColumnResultSet reducedData = new GenerateReducedDataOp().generateReducedData(normalizedTimeSeries, sampleFunctions);
    RowColumnResultSet measurementErrors = new ComputeMeasurementErrorOp().computeNormalizedMeasurementError(sampleFunctions, indexOfFirstPostbleach, rawTimeSeriesImages, prebleachAvg, null);
    ErrorFunction errorFunction = new ErrorFunctionKenworthy(reducedData);
    // 
    // 2 parameter uniform disk model
    // 
    OptModel uniformDisk2OptModel = new OptModelKenworthyUniformDisk2P(bleachRadius_ROI);
    String title_u2 = "Uniform Disk Model - 2 parameters, (Rn=" + bleachRadius_ROI + ")";
    OptContext uniformDisk2Context = new Generate2DOptContextOp().generate2DOptContext(uniformDisk2OptModel, reducedData, measurementErrors, errorFunction);
    new DisplayInteractiveModelOp().displayOptModel(uniformDisk2Context, sampleFunctions, localWorkspace, title_u2, null);
    // 
    // 3 parameter uniform disk model
    // 
    OptModel uniformDisk3OptModel = new OptModelKenworthyUniformDisk3P(bleachRadius_ROI);
    OptContext uniformDisk3Context = new Generate2DOptContextOp().generate2DOptContext(uniformDisk3OptModel, reducedData, measurementErrors, errorFunction);
    String title_u3 = "Uniform Disk Model - 3 parameters, (Rn=" + bleachRadius_ROI + ")";
    new DisplayInteractiveModelOp().displayOptModel(uniformDisk3Context, sampleFunctions, localWorkspace, title_u3, null);
    // 
    // GaussianFit parameter uniform disk model
    // 
    FloatImage prebleachBleachAreaImage = new FloatImage(prebleachAvg);
    // mask-out all but the bleach area
    prebleachBleachAreaImage.and(bleachROI.getRoiImages()[0]);
    double prebleachAvgInROI = prebleachBleachAreaImage.getImageStatistics().meanValue;
    OptModel gaussian2OptModel = new OptModelKenworthyGaussian(prebleachAvgInROI, bleachFactorK_GaussianFit, bleachRadius_GaussianFit, bleachRadius_ROI);
    OptContext gaussianDisk2Context = new Generate2DOptContextOp().generate2DOptContext(gaussian2OptModel, reducedData, measurementErrors, errorFunction);
    String title_g2 = "Gaussian Disk Model - 2 parameters (prebleach=" + prebleachAvgInROI + ",K=" + bleachFactorK_GaussianFit + ",Re=" + bleachRadius_GaussianFit + ",Rnom=" + bleachRadius_ROI + ")";
    new DisplayInteractiveModelOp().displayOptModel(gaussianDisk2Context, sampleFunctions, localWorkspace, title_g2, null);
}