Examples with ProfileData org.vcell.optimization.ProfileData used on opensource projects

Example 6 with ProfileData

use of org.vcell.optimization.ProfileData in project vcell by virtualcell.

the class FRAPDiffTwoParamPanel method showParameterEvaluation.

public void showParameterEvaluation() {
    AsynchClientTask evaluateTask = new AsynchClientTask("Prepare to evaluate parameters ...", AsynchClientTask.TASKTYPE_NONSWING_BLOCKING) {

        public void run(Hashtable<String, Object> hashTable) throws Exception {
            String errorStr = checkParameters();
            if (!errorStr.equals("")) {
                throw new IllegalArgumentException(errorStr);
            }
        }
    };
    AsynchClientTask showResultTask = new AsynchClientTask("Showing profile likelihood and confidence intervals ...", AsynchClientTask.TASKTYPE_SWING_BLOCKING) {

        public void run(Hashtable<String, Object> hashTable) throws Exception {
            ProfileData[] profileData = frapOptData.getExpFrapStudy().getProfileData_twoDiffComponents();
            if (profileData != null && profileData.length > 0) {
                JPanel basePanel = new JPanel();
                // put plotpanes of different parameters' profile likelihoods into a base panel
                basePanel.setLayout(new BoxLayout(basePanel, BoxLayout.Y_AXIS));
                for (int i = 0; i < profileData.length; i++) {
                    ConfidenceIntervalPlotPanel plotPanel = new ConfidenceIntervalPlotPanel();
                    plotPanel.setProfileSummaryData(FRAPOptimizationUtils.getSummaryFromProfileData(profileData[i]));
                    plotPanel.setBorder(new EtchedBorder());
                    String paramName = "";
                    if (profileData[i].getProfileDataElements().size() > 0) {
                        paramName = profileData[i].getProfileDataElements().get(0).getParamName();
                    }
                    ProfileDataPanel profileDataPanel = new ProfileDataPanel(plotPanel, paramName);
                    basePanel.add(profileDataPanel);
                }
                JScrollPane scrollPane = new JScrollPane(basePanel);
                scrollPane.setAutoscrolls(true);
                scrollPane.setPreferredSize(new Dimension(620, 600));
                scrollPane.setHorizontalScrollBarPolicy(JScrollPane.HORIZONTAL_SCROLLBAR_AS_NEEDED);
                scrollPane.setVerticalScrollBarPolicy(JScrollPane.VERTICAL_SCROLLBAR_AS_NEEDED);
                // show plots in a dialog
                DialogUtils.showComponentCloseDialog(FRAPDiffTwoParamPanel.this, scrollPane, "Profile Likelihood of Parameters");
            }
        }
    };
    // dispatch
    ClientTaskDispatcher.dispatch(FRAPDiffTwoParamPanel.this, new Hashtable<String, Object>(), new AsynchClientTask[] { evaluateTask, showResultTask }, false, true, null, true);
}

Example 7 with ProfileData

use of org.vcell.optimization.ProfileData in project vcell by virtualcell.

the class FRAPReacOffRateParametersPanel method showParameterEvaluation.

public void showParameterEvaluation() {
    AsynchClientTask evaluateTask = new AsynchClientTask("Prepare to evaluate parameters ...", AsynchClientTask.TASKTYPE_NONSWING_BLOCKING) {

        public void run(Hashtable<String, Object> hashTable) throws Exception {
            String errorStr = checkParameters();
            if (!errorStr.equals("")) {
                throw new IllegalArgumentException(errorStr);
            }
        }
    };
    AsynchClientTask showResultTask = new AsynchClientTask("Showing profile likelihood and confidence intervals ...", AsynchClientTask.TASKTYPE_SWING_BLOCKING) {

        public void run(Hashtable<String, Object> hashTable) throws Exception {
            ProfileData[] profileData = frapWorkspace.getWorkingFrapStudy().getProfileData_reactionOffRate();
            if (profileData != null && profileData.length > 0) {
                // put plotpanes of different parameters' profile likelihoods into a base panel
                JPanel basePanel = new JPanel();
                basePanel.setLayout(new BoxLayout(basePanel, BoxLayout.Y_AXIS));
                for (int i = 0; i < profileData.length; i++) {
                    ConfidenceIntervalPlotPanel plotPanel = new ConfidenceIntervalPlotPanel();
                    plotPanel.setProfileSummaryData(FRAPOptimizationUtils.getSummaryFromProfileData(profileData[i]));
                    plotPanel.setBorder(new EtchedBorder());
                    String paramName = "";
                    if (profileData[i].getProfileDataElements().size() > 0) {
                        paramName = profileData[i].getProfileDataElements().get(0).getParamName();
                    }
                    ProfileDataPanel profileDataPanel = new ProfileDataPanel(plotPanel, paramName);
                    basePanel.add(profileDataPanel);
                }
                JScrollPane scrollPane = new JScrollPane(basePanel);
                scrollPane.setAutoscrolls(true);
                scrollPane.setPreferredSize(new Dimension(620, 600));
                scrollPane.setHorizontalScrollBarPolicy(JScrollPane.HORIZONTAL_SCROLLBAR_AS_NEEDED);
                scrollPane.setVerticalScrollBarPolicy(JScrollPane.VERTICAL_SCROLLBAR_AS_NEEDED);
                // show plots in a dialog
                DialogUtils.showComponentCloseDialog(FRAPReacOffRateParametersPanel.this, scrollPane, "Profile Likelihood of Parameters");
            }
        }
    };
    // dispatch
    ClientTaskDispatcher.dispatch(FRAPReacOffRateParametersPanel.this, new Hashtable<String, Object>(), new AsynchClientTask[] { evaluateTask, showResultTask }, false, true, null, true);
}

Example 8 with ProfileData

use of org.vcell.optimization.ProfileData in project vcell by virtualcell.

the class RunProfileLikelihoodGeneralOp method evaluateParameters.

private ProfileData[] evaluateParameters(OptContext optContext, Parameter[] currentParams, ClientTaskStatusSupport clientTaskStatusSupport) throws Exception {
    // long startTime =System.currentTimeMillis();
    int totalParamLen = currentParams.length;
    ProfileData[] resultData = new ProfileData[totalParamLen];
    for (int j = 0; j < totalParamLen; j++) {
        ProfileData profileData = new ProfileData();
        // add the fixed parameter to profileData, output exp data and opt results
        Parameter[] newBestParameters = getBestParameters(optContext, currentParams, null);
        double iniError = leastError;
        // fixed parameter
        Parameter fixedParam = newBestParameters[j];
        if (// log function cannot take 0 as parameter
        fixedParam.getInitialGuess() == 0) {
            fixedParam = new Parameter(fixedParam.getName(), fixedParam.getLowerBound(), fixedParam.getUpperBound(), fixedParam.getScale(), epsilon);
        }
        if (clientTaskStatusSupport != null) {
            clientTaskStatusSupport.setMessage("<html>Evaluating confidence intervals of \'" + fixedParam.getName() + "\' <br> of model '" + optContext.getModelName() + "'.</html>");
        }
        ProfileDataElement pde = new ProfileDataElement(fixedParam.getName(), Math.log10(fixedParam.getInitialGuess()), iniError, newBestParameters);
        profileData.addElement(pde);
        Parameter[] unFixedParams = new Parameter[totalParamLen - 1];
        int indexCounter = 0;
        for (int i = 0; i < totalParamLen; i++) {
            if (!newBestParameters[i].getName().equals(fixedParam.getName())) {
                unFixedParams[indexCounter] = newBestParameters[i];
                indexCounter++;
            } else
                continue;
        }
        // increase
        int iterationCount = 1;
        double paramLogVal = Math.log10(fixedParam.getInitialGuess());
        double lastError = iniError;
        boolean isBoundReached = false;
        double incrementStep = CONFIDENCE_INTERVAL_STEP;
        int stepIncreaseCount = 0;
        while (true) {
            if (// if exceeds the maximum iterations, break;
            iterationCount > MAX_ITERATION) {
                break;
            }
            if (isBoundReached) {
                break;
            }
            paramLogVal = paramLogVal + incrementStep;
            double paramVal = Math.pow(10, paramLogVal);
            if (paramVal > (fixedParam.getUpperBound() - epsilon)) {
                paramVal = fixedParam.getUpperBound() - epsilon;
                paramLogVal = Math.log10(fixedParam.getUpperBound());
                isBoundReached = true;
            }
            Parameter increasedParam = new Parameter(fixedParam.getName(), fixedParam.getLowerBound(), fixedParam.getUpperBound(), fixedParam.getScale(), paramVal);
            // getBestParameters returns the whole set of parameters including the fixed parameters
            optContext.setFixedParameter(fixedParam, paramVal);
            Parameter[] newParameters = getBestParameters(optContext, unFixedParams, increasedParam);
            for (// use last step unfixed parameter values to optimize
            int i = 0; // use last step unfixed parameter values to optimize
            i < newParameters.length; // use last step unfixed parameter values to optimize
            i++) {
                for (int k = 0; k < unFixedParams.length; k++) {
                    if (newParameters[i].getName().equals(unFixedParams[k].getName())) {
                        Parameter tempParameter = new Parameter(unFixedParams[k].getName(), unFixedParams[k].getLowerBound(), unFixedParams[k].getUpperBound(), unFixedParams[k].getScale(), newParameters[i].getInitialGuess());
                        unFixedParams[k] = tempParameter;
                    }
                }
            }
            double error = leastError;
            pde = new ProfileDataElement(increasedParam.getName(), paramLogVal, error, newParameters);
            profileData.addElement(pde);
            // check if the we run enough to get confidence intervals(99% @6.635, we plus 10 over the min error)
            if (error > (iniError + 10)) {
                break;
            }
            if (Math.abs((error - lastError) / lastError) < MIN_LIKELIHOOD_CHANGE) {
                stepIncreaseCount++;
                incrementStep = CONFIDENCE_INTERVAL_STEP * Math.pow(2, stepIncreaseCount);
            } else {
                if (stepIncreaseCount > 1) {
                    incrementStep = CONFIDENCE_INTERVAL_STEP / Math.pow(2, stepIncreaseCount);
                    stepIncreaseCount--;
                }
            }
            if (clientTaskStatusSupport != null && clientTaskStatusSupport.isInterrupted()) {
                throw UserCancelException.CANCEL_GENERIC;
            }
            lastError = error;
            iterationCount++;
            if (clientTaskStatusSupport != null) {
                clientTaskStatusSupport.setProgress((int) ((iterationCount * 1.0 / MAX_ITERATION) * 0.5 * 100));
            }
        }
        if (clientTaskStatusSupport != null) {
            // half way through evaluation of a parameter.
            clientTaskStatusSupport.setProgress(50);
        }
        // decrease
        iterationCount = 1;
        paramLogVal = Math.log10(fixedParam.getInitialGuess());
        ;
        ;
        lastError = iniError;
        isBoundReached = false;
        double decrementStep = incrementStep;
        stepIncreaseCount = 0;
        while (true) {
            if (// if exceeds the maximum iterations, break;
            iterationCount > MAX_ITERATION) {
                break;
            }
            if (isBoundReached) {
                break;
            }
            paramLogVal = paramLogVal - decrementStep;
            double paramVal = Math.pow(10, paramLogVal);
            if (paramVal < (fixedParam.getLowerBound() + epsilon)) {
                paramVal = fixedParam.getLowerBound() + epsilon;
                paramLogVal = Math.log10(epsilon);
                isBoundReached = true;
            }
            Parameter decreasedParam = new Parameter(fixedParam.getName(), fixedParam.getLowerBound(), fixedParam.getUpperBound(), fixedParam.getScale(), paramVal);
            // getBestParameters returns the whole set of parameters including the fixed parameters
            optContext.setFixedParameter(fixedParam, paramVal);
            Parameter[] newParameters = getBestParameters(optContext, unFixedParams, decreasedParam);
            for (// use last step unfixed parameter values to optimize
            int i = 0; // use last step unfixed parameter values to optimize
            i < newParameters.length; // use last step unfixed parameter values to optimize
            i++) {
                for (int k = 0; k < unFixedParams.length; k++) {
                    if (newParameters[i].getName().equals(unFixedParams[k].getName())) {
                        Parameter tempParameter = new Parameter(unFixedParams[k].getName(), unFixedParams[k].getLowerBound(), unFixedParams[k].getUpperBound(), unFixedParams[k].getScale(), newParameters[i].getInitialGuess());
                        unFixedParams[k] = tempParameter;
                    }
                }
            }
            double error = leastError;
            pde = new ProfileDataElement(decreasedParam.getName(), paramLogVal, error, newParameters);
            profileData.addElement(0, pde);
            if (error > (iniError + 10)) {
                break;
            }
            if (Math.abs((error - lastError) / lastError) < MIN_LIKELIHOOD_CHANGE) {
                stepIncreaseCount++;
                decrementStep = CONFIDENCE_INTERVAL_STEP * Math.pow(2, stepIncreaseCount);
            } else {
                if (stepIncreaseCount > 1) {
                    incrementStep = CONFIDENCE_INTERVAL_STEP / Math.pow(2, stepIncreaseCount);
                    stepIncreaseCount--;
                }
            }
            if (clientTaskStatusSupport != null && clientTaskStatusSupport.isInterrupted()) {
                throw UserCancelException.CANCEL_GENERIC;
            }
            lastError = error;
            iterationCount++;
            if (clientTaskStatusSupport != null) {
                clientTaskStatusSupport.setProgress((int) (((iterationCount + MAX_ITERATION) * 1.0 / MAX_ITERATION) * 0.5 * 100));
            }
        }
        resultData[j] = profileData;
        if (clientTaskStatusSupport != null) {
            // finish evaluation of a parameter
            clientTaskStatusSupport.setProgress(100);
        }
    }
    optContext.clearFixedParameter();
    // this message is specifically set for batchrun, the message will stay in the status panel. It doesn't affect single run,which disappears quickly that user won't notice.
    if (clientTaskStatusSupport != null) {
        clientTaskStatusSupport.setMessage("Evaluating confidence intervals ...");
    }
    // System.out.println("total time used:" + (endTime - startTime));
    return resultData;
}

Example 9 with ProfileData

use of org.vcell.optimization.ProfileData in project vcell by virtualcell.

the class VFrapProcess method compute.

public static VFrapProcessResults compute(ImageTimeSeries rawTimeSeriesImages, double bleachThreshold, double cellThreshold, LocalWorkspace localWorkspace, ClientTaskStatusSupport clientTaskStatusSupport) throws Exception {
    GenerateCellROIsFromRawFrapTimeSeriesOp generateCellROIs = new GenerateCellROIsFromRawFrapTimeSeriesOp();
    GeometryRoisAndBleachTiming geometryAndTiming = generateCellROIs.generate(rawTimeSeriesImages, cellThreshold);
    GenerateNormalizedFrapDataOp generateNormalizedFrapData = new GenerateNormalizedFrapDataOp();
    NormalizedFrapDataResults normalizedFrapResults = generateNormalizedFrapData.generate(rawTimeSeriesImages, geometryAndTiming.backgroundROI_2D, geometryAndTiming.indexOfFirstPostbleach);
    GenerateBleachRoiOp generateROIs = new GenerateBleachRoiOp();
    ROI bleachROI = generateROIs.generateBleachRoi(normalizedFrapResults.normalizedFrapData.getAllImages()[0], geometryAndTiming.cellROI_2D, bleachThreshold);
    GenerateDependentImageROIsOp generateDependentROIs = new GenerateDependentImageROIsOp();
    ROI[] dataROIs = generateDependentROIs.generate(geometryAndTiming.cellROI_2D, bleachROI);
    NormalizedSampleFunction[] roiSampleFunctions = new NormalizedSampleFunction[dataROIs.length];
    for (int i = 0; i < dataROIs.length; i++) {
        roiSampleFunctions[i] = NormalizedSampleFunction.fromROI(dataROIs[i]);
    }
    GenerateReducedDataOp generateReducedNormalizedData = new GenerateReducedDataOp();
    RowColumnResultSet reducedData = generateReducedNormalizedData.generateReducedData(normalizedFrapResults.normalizedFrapData, roiSampleFunctions);
    ComputeMeasurementErrorOp computeMeasurementError = new ComputeMeasurementErrorOp();
    RowColumnResultSet measurementError = computeMeasurementError.computeNormalizedMeasurementError(roiSampleFunctions, geometryAndTiming.indexOfFirstPostbleach, rawTimeSeriesImages, normalizedFrapResults.prebleachAverage, clientTaskStatusSupport);
    GenerateTrivial2DPsfOp psf_2D = new GenerateTrivial2DPsfOp();
    UShortImage psf = psf_2D.generateTrivial2D_Psf();
    // RunRefSimulationOp runRefSimulationFull = new RunRefSimulationOp();
    // GenerateReducedROIDataOp generateReducedRefSimData = new GenerateReducedROIDataOp();
    RunRefSimulationFastOp runRefSimulationFast = new RunRefSimulationFastOp();
    RowColumnResultSet refData = runRefSimulationFast.runRefSimFast(geometryAndTiming.cellROI_2D, normalizedFrapResults.normalizedFrapData, dataROIs, psf, localWorkspace, clientTaskStatusSupport);
    final double refDiffusionRate = 1.0;
    double[] refSimTimePoints = refData.extractColumn(0);
    int numRois = refData.getDataColumnCount() - 1;
    int numRefSimTimes = refData.getRowCount();
    double[][] refSimData = new double[numRois][numRefSimTimes];
    for (int roi = 0; roi < numRois; roi++) {
        double[] roiData = refData.extractColumn(roi + 1);
        for (int t = 0; t < numRefSimTimes; t++) {
            refSimData[roi][t] = roiData[t];
        }
    }
    ErrorFunction errorFunction = new ErrorFunctionNoiseWeightedL2();
    OptModelOneDiff optModelOneDiff = new OptModelOneDiff(refSimData, refSimTimePoints, refDiffusionRate);
    Generate2DOptContextOp generate2DOptContextOne = new Generate2DOptContextOp();
    OptContext optContextOneDiff = generate2DOptContextOne.generate2DOptContext(optModelOneDiff, reducedData, measurementError, errorFunction);
    RunProfileLikelihoodGeneralOp runProfileLikelihoodOne = new RunProfileLikelihoodGeneralOp();
    ProfileData[] profileDataOne = runProfileLikelihoodOne.runProfileLikihood(optContextOneDiff, clientTaskStatusSupport);
    // OptModelTwoDiffWithoutPenalty optModelTwoDiffWithoutPenalty = new OptModelTwoDiffWithoutPenalty(refSimData, refSimTimePoints, refDiffusionRate);
    // Generate2DOptContextOp generate2DOptContextTwoWithoutPenalty = new Generate2DOptContextOp();
    // OptContext optContextTwoDiffWithoutPenalty = generate2DOptContextTwoWithoutPenalty.generate2DOptContext(optModelTwoDiffWithoutPenalty, reducedData, measurementError);
    // RunProfileLikelihoodGeneralOp runProfileLikelihoodTwoWithoutPenalty = new RunProfileLikelihoodGeneralOp();
    // ProfileData[] profileDataTwoWithoutPenalty = runProfileLikelihoodTwoWithoutPenalty.runProfileLikihood(optContextTwoDiffWithoutPenalty, clientTaskStatusSupport);
    OptModelTwoDiffWithPenalty optModelTwoDiffWithPenalty = new OptModelTwoDiffWithPenalty(refSimData, refSimTimePoints, refDiffusionRate);
    Generate2DOptContextOp generate2DOptContextTwoWithPenalty = new Generate2DOptContextOp();
    OptContext optContextTwoDiffWithPenalty = generate2DOptContextTwoWithPenalty.generate2DOptContext(optModelTwoDiffWithPenalty, reducedData, measurementError, errorFunction);
    RunProfileLikelihoodGeneralOp runProfileLikelihoodTwoWithPenalty = new RunProfileLikelihoodGeneralOp();
    ProfileData[] profileDataTwoWithPenalty = runProfileLikelihoodTwoWithPenalty.runProfileLikihood(optContextTwoDiffWithPenalty, clientTaskStatusSupport);
    // 
    // SLOW WAY
    // 
    // runRefSimulationFull.cellROI_2D,generateCellROIs.cellROI_2D);
    // runRefSimulationFull.normalizedTimeSeries,generateNormalizedFrapData.normalizedFrapData);
    // workflow.addTask(runRefSimulationFull);
    // generateReducedRefSimData.imageTimeSeries,runRefSimulationFull.refSimTimeSeries);
    // generateReducedRefSimData.imageDataROIs,generateDependentROIs.imageDataROIs);
    // workflow.addTask(generateReducedRefSimData);
    // DataHolder<RowColumnResultSet> reducedROIData = generateReducedRefSimData.reducedROIData;
    // DataHolder<Double> refSimDiffusionRate = runRefSimulationFull.refSimDiffusionRate;
    VFrapProcessResults results = new VFrapProcessResults(dataROIs, geometryAndTiming.cellROI_2D, bleachROI, normalizedFrapResults.normalizedFrapData, reducedData, profileDataOne, profileDataTwoWithPenalty);
    return results;
}

Example 10 with ProfileData

use of org.vcell.optimization.ProfileData in project vcell by virtualcell.

the class WorkflowObjectsPanel method displayData.

private void displayData(TaskContext taskContext, WorkflowObject workflowObject) {
    if (workflowObject instanceof DataInput || workflowObject instanceof DataOutput) {
        String title = parametersFunctionsTableModel.getName(workflowObject);
        WindowListener listener = new WindowAdapter() {
        };
        Object data = null;
        if (workflowObject instanceof WorkflowDataSource) {
            WorkflowDataSource dataHolder = (WorkflowDataSource) workflowObject;
            data = taskContext.getRepository().getData(dataHolder);
        } else if (workflowObject instanceof DataInput) {
            DataInput dataInput = (DataInput) workflowObject;
            data = taskContext.getData(dataInput);
        }
        if (data instanceof RowColumnResultSet) {
            RowColumnResultSet rc = (RowColumnResultSet) data;
            try {
                new DisplayPlotOp().displayPlot(rc, title, listener);
            } catch (ExpressionException e) {
                e.printStackTrace();
            }
        } else if (data instanceof ROI) {
            ROI roi = (ROI) data;
            Image image = roi.getRoiImages()[0];
            new DisplayImageOp().displayImage(image, title, listener);
        } else if (data instanceof ProfileData[]) {
            ProfileData[] profileData = (ProfileData[]) data;
            new DisplayProfileLikelihoodPlotsOp().displayProfileLikelihoodPlots(profileData, title, listener);
        } else if (data instanceof Image) {
            Image image = (Image) data;
            new DisplayImageOp().displayImage(image, title, listener);
        } else if (data instanceof ImageTimeSeries) {
            ImageTimeSeries imageTimeSeries = (ImageTimeSeries) data;
            try {
                DisplayTimeSeries.displayImageTimeSeries(imageTimeSeries, title, listener);
            } catch (ImageException | IOException e) {
                e.printStackTrace();
            }
        }
    }
}