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

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Example 11 with ProfileData

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

the class WorkflowObjectsPanel method hasDisplayData.

private boolean hasDisplayData(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 || data instanceof ROI || data instanceof ProfileData[] || data instanceof Image || data instanceof ImageTimeSeries) {
            return true;
        }
    }
    return false;
}
Also used : DataOutput(org.vcell.workflow.DataOutput) WindowListener(java.awt.event.WindowListener) WindowAdapter(java.awt.event.WindowAdapter) ProfileData(org.vcell.optimization.ProfileData) Image(cbit.vcell.VirtualMicroscopy.Image) ROI(cbit.vcell.VirtualMicroscopy.ROI) WorkflowDataSource(org.vcell.workflow.WorkflowDataSource) DataInput(org.vcell.workflow.DataInput) WorkflowObject(org.vcell.workflow.WorkflowObject) RowColumnResultSet(cbit.vcell.math.RowColumnResultSet) ImageTimeSeries(org.vcell.vmicro.workflow.data.ImageTimeSeries)

Example 12 with ProfileData

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

the class VirtualFrapBatchRunFrame method getBatchRunTasks.

private ArrayList<AsynchClientTask> getBatchRunTasks() {
    // to run batch files
    ArrayList<AsynchClientTask> batchRunTaskList = new ArrayList<AsynchClientTask>();
    for (int i = 0; i < batchRunWorkspace.getFrapStudies().size(); i++) {
        final int finalIdx = i;
        AsynchClientTask message1Task = new AsynchClientTask("Preparing for parameter estimation ...", AsynchClientTask.TASKTYPE_SWING_BLOCKING) {

            public void run(Hashtable<String, Object> hashTable) throws Exception {
                appendJobStatus("<html><br>Running " + (batchRunWorkspace.getFrapStudies().get(finalIdx)).getXmlFilename() + "</html>", false);
            }
        };
        AsynchClientTask saveTask = new AsynchClientTask("Preparing for parameter estimation ...", AsynchClientTask.TASKTYPE_NONSWING_BLOCKING) {

            public void run(Hashtable<String, Object> hashTable) throws Exception {
                FRAPStudy fStudy = (batchRunWorkspace.getFrapStudies().get(finalIdx));
                if (fStudy.hasDiffusionOnlyModel()) {
                    // check external data info. If not existing,  we have to run ref simulation.
                    if (!FRAPWorkspace.areExternalDataOK(localWorkspace, fStudy.getFrapDataExternalDataInfo(), fStudy.getRoiExternalDataInfo())) {
                        // if external files are missing/currupt or ROIs are changed, create keys and save them
                        fStudy.setFrapDataExternalDataInfo(FRAPStudy.createNewExternalDataInfo(localWorkspace, FRAPStudy.IMAGE_EXTDATA_NAME));
                        fStudy.setRoiExternalDataInfo(FRAPStudy.createNewExternalDataInfo(localWorkspace, FRAPStudy.ROI_EXTDATA_NAME));
                        fStudy.saveROIsAsExternalData(localWorkspace, fStudy.getRoiExternalDataInfo().getExternalDataIdentifier(), fStudy.getStartingIndexForRecovery());
                        fStudy.saveImageDatasetAsExternalData(localWorkspace, fStudy.getFrapDataExternalDataInfo().getExternalDataIdentifier(), fStudy.getStartingIndexForRecovery());
                    }
                }
            }
        };
        AsynchClientTask message2Task = new AsynchClientTask("Running reference simulation ...", AsynchClientTask.TASKTYPE_SWING_BLOCKING) {

            public void run(Hashtable<String, Object> hashTable) throws Exception {
                FRAPStudy fStudy = (batchRunWorkspace.getFrapStudies().get(finalIdx));
                if (fStudy.hasDiffusionOnlyModel()) {
                    MessagePanel msgPanel = appendJobStatus("Running reference simulation ...", true);
                    hashTable.put("runRefStatus", msgPanel);
                } else if (fStudy.hasReactionOnlyOffRateModel()) {
                // MessagePanel msgPanel = appendJobStatus("...", true);
                // hashTable.put("runRefStatus", msgPanel);
                }
            }
        };
        AsynchClientTask runRefSimTask = new AsynchClientTask("Running reference simulation ...", AsynchClientTask.TASKTYPE_NONSWING_BLOCKING) {

            public void run(Hashtable<String, Object> hashTable) throws Exception {
                FRAPStudy fStudy = (batchRunWorkspace.getFrapStudies().get(finalIdx));
                if (fStudy.hasDiffusionOnlyModel()) {
                    MessagePanel msgPanel = (MessagePanel) hashTable.get("runRefStatus");
                    // run ref sim
                    if (// if ref data is stored ,we don't have to re-run
                    fStudy.getStoredRefData() != null) {
                        fStudy.setFrapOptData(new FRAPOptData(fStudy, FRAPModel.NUM_MODEL_PARAMETERS_ONE_DIFF, localWorkspace, fStudy.getStoredRefData()));
                    } else {
                        fStudy.setFrapOptData(new FRAPOptData(fStudy, FRAPModel.NUM_MODEL_PARAMETERS_ONE_DIFF, localWorkspace, msgPanel));
                    }
                } else if (fStudy.hasReactionOnlyOffRateModel()) {
                    if (fStudy.getFrapOptFunc() == null) {
                        fStudy.setFrapOptFunc(new FRAPOptFunctions(fStudy));
                    }
                }
            }
        };
        AsynchClientTask message3Task = new AsynchClientTask("Running optimization ...", AsynchClientTask.TASKTYPE_SWING_BLOCKING) {

            public void run(Hashtable<String, Object> hashTable) throws Exception {
                FRAPStudy fStudy = (batchRunWorkspace.getFrapStudies().get(finalIdx));
                if (fStudy.hasDiffusionOnlyModel()) {
                    MessagePanel msgPanel = (MessagePanel) hashTable.get("runRefStatus");
                    msgPanel.setProgressCompleted();
                }
                MessagePanel msgPanel1 = appendJobStatus("Running optimization ...", true);
                hashTable.put("optimizationStatus", msgPanel1);
            }
        };
        AsynchClientTask runOptTask = new AsynchClientTask("Running optimization ...", AsynchClientTask.TASKTYPE_NONSWING_BLOCKING) {

            public void run(Hashtable<String, Object> hashTable) throws Exception {
                FRAPStudy fStudy = (batchRunWorkspace.getFrapStudies().get(finalIdx));
                ArrayList<Integer> models = fStudy.getSelectedModels();
                if (models.size() == 1) {
                    for (int i = 0; i < models.size(); i++) {
                        int model = (models.get(i)).intValue();
                        if (model == batchRunWorkspace.getSelectedModel()) {
                            if (model == FRAPModel.IDX_MODEL_DIFF_ONE_COMPONENT) {
                                // if(fStudy.getModels()[FRAPModel.IDX_MODEL_DIFF_ONE_COMPONENT].getModelParameters() == null)
                                // {//always run
                                fStudy.getFrapOptData().setNumEstimatedParams(FRAPModel.NUM_MODEL_PARAMETERS_ONE_DIFF);
                                Parameter[] initialParams = FRAPModel.getInitialParameters(fStudy.getFrapData(), FRAPModel.MODEL_TYPE_ARRAY[FRAPModel.IDX_MODEL_DIFF_ONE_COMPONENT], fStudy.getStartingIndexForRecovery());
                                // set model parameters and data
                                Parameter[] bestParameters = fStudy.getFrapOptData().getBestParamters(initialParams, fStudy.getSelectedROIsForErrorCalculation());
                                double[][] fitData = fStudy.getFrapOptData().getFitData(bestParameters);
                                fStudy.getModels()[FRAPModel.IDX_MODEL_DIFF_ONE_COMPONENT].setModelParameters(bestParameters);
                                fStudy.getModels()[FRAPModel.IDX_MODEL_DIFF_ONE_COMPONENT].setData(fitData);
                            // }
                            } else if (model == FRAPModel.IDX_MODEL_DIFF_TWO_COMPONENTS) {
                                // if(fStudy.getModels()[FRAPModel.IDX_MODEL_DIFF_TWO_COMPONENTS].getModelParameters() == null)
                                // {//always run
                                fStudy.getFrapOptData().setNumEstimatedParams(FRAPModel.NUM_MODEL_PARAMETERS_TWO_DIFF);
                                Parameter[] initialParams = FRAPModel.getInitialParameters(fStudy.getFrapData(), FRAPModel.MODEL_TYPE_ARRAY[FRAPModel.IDX_MODEL_DIFF_TWO_COMPONENTS], fStudy.getStartingIndexForRecovery());
                                // set model parameters and data
                                Parameter[] bestParameters = fStudy.getFrapOptData().getBestParamters(initialParams, fStudy.getSelectedROIsForErrorCalculation());
                                double[][] fitData = fStudy.getFrapOptData().getFitData(bestParameters);
                                fStudy.getModels()[FRAPModel.IDX_MODEL_DIFF_TWO_COMPONENTS].setModelParameters(bestParameters);
                                fStudy.getModels()[FRAPModel.IDX_MODEL_DIFF_TWO_COMPONENTS].setData(fitData);
                            // }
                            } else if (model == FRAPModel.IDX_MODEL_REACTION_OFF_RATE) {
                                Parameter[] bestParameters = fStudy.getFrapOptFunc().getBestParamters(fStudy.getFrapData(), null, true);
                                double[][] fitData = fStudy.getFrapOptFunc().getFitData(bestParameters);
                                fStudy.getModels()[FRAPModel.IDX_MODEL_REACTION_OFF_RATE].setModelParameters(bestParameters);
                                fStudy.getModels()[FRAPModel.IDX_MODEL_REACTION_OFF_RATE].setData(fitData);
                            }
                        } else {
                            throw new Exception("Selected model for batch run is not the same as selected model in FRAP doc " + fStudy.getXmlFilename());
                        }
                    }
                } else {
                    throw new Exception("Selected model size exceed 1");
                }
            }
        };
        AsynchClientTask message4Task = new AsynchClientTask("Evaluating confidence intervals for parameters ...", AsynchClientTask.TASKTYPE_SWING_BLOCKING) {

            public void run(Hashtable<String, Object> hashTable) throws Exception {
                MessagePanel msgPanel = (MessagePanel) hashTable.get("optimizationStatus");
                msgPanel.setProgressCompleted();
                MessagePanel msgPanel2 = appendJobStatus("Evaluating confidence intervals for parameters ...", true);
                hashTable.put("evaluateCI", msgPanel2);
            }
        };
        AsynchClientTask evaluateCITask = new AsynchClientTask("Evaluating confidence intervals for parameters ...", AsynchClientTask.TASKTYPE_NONSWING_BLOCKING) {

            public void run(Hashtable<String, Object> hashTable) throws Exception {
                FRAPStudy fStudy = (batchRunWorkspace.getFrapStudies().get(finalIdx));
                MessagePanel msgPanel = (MessagePanel) hashTable.get("evaluateCI");
                // evaluate confidence intervals
                ArrayList<Integer> models = fStudy.getSelectedModels();
                if (models.size() == 1) {
                    for (int i = 0; i < models.size(); i++) {
                        int model = (models.get(i)).intValue();
                        if (model == batchRunWorkspace.getSelectedModel()) {
                            if (model == FRAPModel.IDX_MODEL_DIFF_ONE_COMPONENT) {
                                if (fStudy.getModels()[FRAPModel.IDX_MODEL_DIFF_ONE_COMPONENT].getModelParameters() != null) {
                                    ProfileData[] profileData = fStudy.getFrapOptData().evaluateParameters(fStudy.getModels()[FRAPModel.IDX_MODEL_DIFF_ONE_COMPONENT].getModelParameters(), msgPanel);
                                    fStudy.setProfileData_oneDiffComponent(profileData);
                                }
                            } else if (model == FRAPModel.IDX_MODEL_DIFF_TWO_COMPONENTS) {
                                if (fStudy.getModels()[FRAPModel.IDX_MODEL_DIFF_TWO_COMPONENTS].getModelParameters() != null) {
                                    ProfileData[] profileData = fStudy.getFrapOptData().evaluateParameters(fStudy.getModels()[FRAPModel.IDX_MODEL_DIFF_TWO_COMPONENTS].getModelParameters(), msgPanel);
                                    fStudy.setProfileData_twoDiffComponents(profileData);
                                }
                            } else if (model == FRAPModel.IDX_MODEL_REACTION_OFF_RATE) {
                                if (fStudy.getModels()[FRAPModel.IDX_MODEL_REACTION_OFF_RATE].getModelParameters() != null) {
                                    ProfileData[] profileData = fStudy.getFrapOptFunc().evaluateParameters(fStudy.getModels()[FRAPModel.IDX_MODEL_REACTION_OFF_RATE].getModelParameters(), msgPanel);
                                    fStudy.setProfileData_reactionOffRate(profileData);
                                }
                            }
                        } else {
                            throw new Exception("Selected model for batch run is not the same as selected model in FRAP doc " + fStudy.getXmlFilename());
                        }
                    }
                } else {
                    throw new Exception("Selected model size exceed 1");
                }
            }
        };
        AsynchClientTask message5Task = new AsynchClientTask("Evaluating confidence intervals for parameters ...", AsynchClientTask.TASKTYPE_SWING_BLOCKING) {

            public void run(Hashtable<String, Object> hashTable) throws Exception {
                MessagePanel msgPanel = (MessagePanel) hashTable.get("evaluateCI");
                msgPanel.setProgressCompleted();
            }
        };
        batchRunTaskList.add(message1Task);
        batchRunTaskList.add(saveTask);
        batchRunTaskList.add(message2Task);
        batchRunTaskList.add(runRefSimTask);
        batchRunTaskList.add(message3Task);
        batchRunTaskList.add(runOptTask);
        batchRunTaskList.add(message4Task);
        batchRunTaskList.add(evaluateCITask);
        batchRunTaskList.add(message5Task);
    }
    return batchRunTaskList;
}
Also used : AsynchClientTask(cbit.vcell.client.task.AsynchClientTask) Hashtable(java.util.Hashtable) FRAPOptFunctions(cbit.vcell.microscopy.FRAPOptFunctions) ArrayList(java.util.ArrayList) ProfileData(org.vcell.optimization.ProfileData) UserCancelException(org.vcell.util.UserCancelException) FRAPStudy(cbit.vcell.microscopy.FRAPStudy) FRAPOptData(cbit.vcell.microscopy.FRAPOptData) Parameter(cbit.vcell.opt.Parameter)

Example 13 with ProfileData

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

the class AnalysisResultsTablePanel method propertyChange.

public void propertyChange(PropertyChangeEvent evt) {
    if (evt.getPropertyName().equals(FRAPSingleWorkspace.PROPERTY_CHANGE_CONFIDENCEINTERVAL_DETAILS)) {
        if (((Integer) evt.getNewValue()).intValue() == AnalysisTableModel.COLUMN_DIFF_ONE_CI_PLOT && getTable().getSelectedRow() < FRAPModel.NUM_MODEL_PARAMETERS_ONE_DIFF) {
            if (frapWorkspace.getWorkingFrapStudy() != null && frapWorkspace.getWorkingFrapStudy().getProfileData_oneDiffComponent() != null) {
                int paramIdx = getTable().getSelectedRow();
                ProfileData[] profileData = frapWorkspace.getWorkingFrapStudy().getProfileData_oneDiffComponent();
                // put plotpanes of different parameters' profile likelihoods into a base panel
                JPanel basePanel = new JPanel();
                basePanel.setLayout(new BoxLayout(basePanel, BoxLayout.Y_AXIS));
                ConfidenceIntervalPlotPanel plotPanel = new ConfidenceIntervalPlotPanel();
                plotPanel.setProfileSummaryData(FRAPOptimizationUtils.getSummaryFromProfileData(profileData[paramIdx]));
                plotPanel.setBorder(new EtchedBorder());
                String paramName = "";
                if (profileData[paramIdx].getProfileDataElements().size() > 0) {
                    paramName = profileData[paramIdx].getProfileDataElements().get(0).getParamName();
                }
                ProfileDataPanel profileDataPanel = new ProfileDataPanel(plotPanel, paramName);
                profileDataPanel.setProfileDataPlotDetailsVisible(true);
                basePanel.add(profileDataPanel);
                JScrollPane scrollPane = new JScrollPane(basePanel);
                scrollPane.setAutoscrolls(true);
                scrollPane.setPreferredSize(new Dimension(600, 600));
                scrollPane.setHorizontalScrollBarPolicy(JScrollPane.HORIZONTAL_SCROLLBAR_AS_NEEDED);
                scrollPane.setVerticalScrollBarPolicy(JScrollPane.VERTICAL_SCROLLBAR_AS_NEEDED);
                // show plots in a dialog
                DialogUtils.showComponentCloseDialog(parent, scrollPane, "Profile Likelihood of Parameters");
            } else {
                DialogUtils.showErrorDialog(parent, "Confidence Intervals haven't been evaluated.");
            }
        } else if (((Integer) evt.getNewValue()).intValue() == AnalysisTableModel.COLUMN_DIFF_TWO_CI_PLOT && getTable().getSelectedRow() < FRAPModel.NUM_MODEL_PARAMETERS_TWO_DIFF) {
            if (frapWorkspace.getWorkingFrapStudy() != null && frapWorkspace.getWorkingFrapStudy().getProfileData_twoDiffComponents() != null) {
                int paramIdx = getTable().getSelectedRow();
                ProfileData[] profileData = frapWorkspace.getWorkingFrapStudy().getProfileData_twoDiffComponents();
                // put plotpanes of different parameters' profile likelihoods into a base panel
                JPanel basePanel = new JPanel();
                basePanel.setLayout(new BoxLayout(basePanel, BoxLayout.Y_AXIS));
                ConfidenceIntervalPlotPanel plotPanel = new ConfidenceIntervalPlotPanel();
                plotPanel.setProfileSummaryData(FRAPOptimizationUtils.getSummaryFromProfileData(profileData[paramIdx]));
                plotPanel.setBorder(new EtchedBorder());
                String paramName = "";
                if (profileData[paramIdx].getProfileDataElements().size() > 0) {
                    paramName = profileData[paramIdx].getProfileDataElements().get(0).getParamName();
                }
                ProfileDataPanel profileDataPanel = new ProfileDataPanel(plotPanel, paramName);
                profileDataPanel.setProfileDataPlotDetailsVisible(true);
                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(parent, scrollPane, "Profile Likelihood of Parameters");
            } else {
                DialogUtils.showErrorDialog(parent, "Confidence Intervals haven't been evaluated.");
            }
        } else if (((Integer) evt.getNewValue()).intValue() == AnalysisTableModel.COLUMN_KOFF_CI_PLOT) {
            if (frapWorkspace.getWorkingFrapStudy() != null && frapWorkspace.getWorkingFrapStudy().getProfileData_reactionOffRate() != null) {
                ProfileData[] profileData = frapWorkspace.getWorkingFrapStudy().getProfileData_reactionOffRate();
                ProfileData tempProfileData = null;
                if (getTable().getSelectedRow() == FRAPModel.INDEX_BLEACH_MONITOR_RATE) {
                    for (ProfileData pData : profileData) {
                        if (pData.getProfileDataElements().size() > 0 && pData.getProfileDataElements().get(0).getParamName().equals(FRAPModel.MODEL_PARAMETER_NAMES[FRAPModel.INDEX_BLEACH_MONITOR_RATE])) {
                            tempProfileData = pData;
                        }
                    }
                } else if (getTable().getSelectedRow() == FRAPModel.INDEX_OFF_RATE) {
                    for (ProfileData pData : profileData) {
                        if (pData.getProfileDataElements().size() > 0 && pData.getProfileDataElements().get(0).getParamName().equals(FRAPModel.MODEL_PARAMETER_NAMES[FRAPModel.INDEX_OFF_RATE])) {
                            tempProfileData = pData;
                        }
                    }
                }
                // put plotpanes of different parameters' profile likelihoods into a base panel
                JPanel basePanel = new JPanel();
                basePanel.setLayout(new BoxLayout(basePanel, BoxLayout.Y_AXIS));
                ConfidenceIntervalPlotPanel plotPanel = new ConfidenceIntervalPlotPanel();
                plotPanel.setProfileSummaryData(FRAPOptimizationUtils.getSummaryFromProfileData(tempProfileData));
                plotPanel.setBorder(new EtchedBorder());
                String paramName = "";
                if (tempProfileData.getProfileDataElements().size() > 0) {
                    paramName = tempProfileData.getProfileDataElements().get(0).getParamName();
                }
                ProfileDataPanel profileDataPanel = new ProfileDataPanel(plotPanel, paramName);
                profileDataPanel.setProfileDataPlotDetailsVisible(true);
                basePanel.add(profileDataPanel);
                JScrollPane scrollPane = new JScrollPane(basePanel);
                scrollPane.setAutoscrolls(true);
                scrollPane.setPreferredSize(new Dimension(600, 600));
                scrollPane.setHorizontalScrollBarPolicy(JScrollPane.HORIZONTAL_SCROLLBAR_AS_NEEDED);
                scrollPane.setVerticalScrollBarPolicy(JScrollPane.VERTICAL_SCROLLBAR_AS_NEEDED);
                // show plots in a dialog
                DialogUtils.showComponentCloseDialog(parent, scrollPane, "Profile Likelihood of Parameters");
            } else {
                DialogUtils.showErrorDialog(parent, "Confidence Intervals haven't been evaluated.");
            }
        }
    }
}
Also used : JScrollPane(javax.swing.JScrollPane) JPanel(javax.swing.JPanel) EtchedBorder(javax.swing.border.EtchedBorder) ConfidenceIntervalPlotPanel(org.vcell.optimization.gui.ConfidenceIntervalPlotPanel) BoxLayout(javax.swing.BoxLayout) ProfileDataPanel(org.vcell.optimization.gui.ProfileDataPanel) ProfileData(org.vcell.optimization.ProfileData) Dimension(java.awt.Dimension)

Example 14 with ProfileData

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

the class FRAPOptData method evaluateParameters.

public ProfileData[] evaluateParameters(Parameter[] currentParams, ClientTaskStatusSupport clientTaskStatusSupport) throws Exception {
    // long startTime =System.currentTimeMillis();
    int totalParamLen = currentParams.length;
    ProfileData[] resultData = new ProfileData[totalParamLen];
    FRAPStudy frapStudy = getExpFrapStudy();
    for (int j = 0; j < totalParamLen; j++) {
        ProfileData profileData = new ProfileData();
        // add the fixed parameter to profileData, output exp data and opt results
        setNumEstimatedParams(totalParamLen);
        Parameter[] newBestParameters = getBestParamters(currentParams, frapStudy.getSelectedROIsForErrorCalculation(), null, true);
        double iniError = getLeastError();
        // 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(), FRAPOptimizationUtils.epsilon);
        }
        if (clientTaskStatusSupport != null) {
            if (totalParamLen == FRAPModel.NUM_MODEL_PARAMETERS_ONE_DIFF) {
                clientTaskStatusSupport.setMessage("<html>Evaluating confidence intervals of \'" + fixedParam.getName() + "\' <br> of diffusion with one diffusing component model.</html>");
            } else if (totalParamLen == FRAPModel.NUM_MODEL_PARAMETERS_TWO_DIFF) {
                clientTaskStatusSupport.setMessage("<html>Evaluating confidence intervals of \'" + fixedParam.getName() + "\' <br> of diffusion with two diffusing components model.</html>");
            }
            // start evaluation of a parameter.
            clientTaskStatusSupport.setProgress(0);
        }
        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 = DEFAULT_CI_STEPS[j];
        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() - FRAPOptimizationUtils.epsilon)) {
                paramVal = fixedParam.getUpperBound();
                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
            setNumEstimatedParams(totalParamLen - 1);
            Parameter[] newParameters = getBestParamters(unFixedParams, frapStudy.getSelectedROIsForErrorCalculation(), increasedParam, true);
            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 = getLeastError();
            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 = DEFAULT_CI_STEPS[j] * Math.pow(2, stepIncreaseCount);
            } else {
                if (stepIncreaseCount > 1) {
                    incrementStep = DEFAULT_CI_STEPS[j] / Math.pow(2, stepIncreaseCount);
                    stepIncreaseCount--;
                }
            }
            if (clientTaskStatusSupport.isInterrupted()) {
                throw UserCancelException.CANCEL_GENERIC;
            }
            lastError = error;
            iterationCount++;
            clientTaskStatusSupport.setProgress((int) ((iterationCount * 1.0 / MAX_ITERATION) * 0.5 * 100));
        }
        // half way through evaluation of a parameter.
        clientTaskStatusSupport.setProgress(50);
        // decrease
        iterationCount = 1;
        paramLogVal = Math.log10(fixedParam.getInitialGuess());
        ;
        ;
        lastError = iniError;
        isBoundReached = false;
        double decrementStep = DEFAULT_CI_STEPS[j];
        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() + FRAPOptimizationUtils.epsilon)) {
                paramVal = FRAPOptimizationUtils.epsilon;
                paramLogVal = Math.log10(FRAPOptimizationUtils.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
            setNumEstimatedParams(totalParamLen - 1);
            Parameter[] newParameters = getBestParamters(unFixedParams, frapStudy.getSelectedROIsForErrorCalculation(), decreasedParam, true);
            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 = getLeastError();
            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 = DEFAULT_CI_STEPS[j] * Math.pow(2, stepIncreaseCount);
            } else {
                if (stepIncreaseCount > 1) {
                    incrementStep = DEFAULT_CI_STEPS[j] / Math.pow(2, stepIncreaseCount);
                    stepIncreaseCount--;
                }
            }
            if (clientTaskStatusSupport.isInterrupted()) {
                throw UserCancelException.CANCEL_GENERIC;
            }
            lastError = error;
            iterationCount++;
            clientTaskStatusSupport.setProgress((int) (((iterationCount + MAX_ITERATION) * 1.0 / MAX_ITERATION) * 0.5 * 100));
        }
        resultData[j] = profileData;
        // finish evaluation of a parameter
        clientTaskStatusSupport.setProgress(100);
    }
    // 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.
    clientTaskStatusSupport.setMessage("Evaluating confidence intervals ...");
    // System.out.println("total time used:" + (endTime - startTime));
    return resultData;
}
Also used : ProfileDataElement(org.vcell.optimization.ProfileDataElement) Parameter(cbit.vcell.opt.Parameter) ProfileData(org.vcell.optimization.ProfileData)

Example 15 with ProfileData

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

the class FRAPParamTest method runProfileLikelihood.

public void runProfileLikelihood() {
    String errorMsg = checkFrapStudyValidity();
    if (!errorMsg.equals("")) {
        System.out.println("Application terminated due to " + errorMsg);
        System.exit(1);
    } else {
        try {
            ClientTaskStatusSupport ctss = new ClientTaskStatusSupport() {

                public void setProgress(int progress) {
                    System.out.println(progress);
                }

                public void setMessage(String message) {
                    System.out.println(message);
                }

                public boolean isInterrupted() {
                    // TODO Auto-generated method stub
                    return false;
                }

                public int getProgress() {
                    // TODO Auto-generated method stub
                    return 0;
                }

                public void addProgressDialogListener(ProgressDialogListener progressDialogListener) {
                    throw new RuntimeException("not yet implemented");
                }
            };
            // get startign index
            if (frapStudy.getStartingIndexForRecovery() == null) {
                int index = FRAPDataAnalysis.calculateRecoveryIndex(frapStudy.getFrapData());
                frapStudy.setStartingIndexForRecovery(index);
            }
            // get dependent rois
            if (frapStudy.getFrapData().getRois().length < 4) {
                frapStudy.refreshDependentROIs();
            }
            // get selected ROIs
            if (frapStudy.getSelectedROIsForErrorCalculation() == null) {
                boolean[] selectedROIs = new boolean[FRAPData.VFRAP_ROI_ENUM.values().length];
                int counter = 0;
                for (FRAPData.VFRAP_ROI_ENUM roiEnum : FRAPData.VFRAP_ROI_ENUM.values()) {
                    if (roiEnum.name().equals(FRAPData.VFRAP_ROI_ENUM.ROI_CELL.name()) || roiEnum.name().equals(FRAPData.VFRAP_ROI_ENUM.ROI_BACKGROUND.name())) {
                        counter++;
                        continue;
                    }
                    if (frapStudy.getFrapData().getRoi(roiEnum.name()).getNonzeroPixelsCount() > 0) {
                        selectedROIs[counter] = true;
                        counter++;
                    }
                }
                frapStudy.setSelectedROIsForErrorCalculation(selectedROIs);
            }
            // get frap opt data
            if (frapStudy.getFrapOptData() == null) {
                if (!FRAPWorkspace.areExternalDataOK(getLocalWorkspace(), frapStudy.getFrapDataExternalDataInfo(), frapStudy.getRoiExternalDataInfo())) {
                    // if external files are missing/currupt or ROIs are changed, create keys and save them
                    frapStudy.setFrapDataExternalDataInfo(FRAPStudy.createNewExternalDataInfo(localWorkspace, FRAPStudy.IMAGE_EXTDATA_NAME));
                    frapStudy.setRoiExternalDataInfo(FRAPStudy.createNewExternalDataInfo(localWorkspace, FRAPStudy.ROI_EXTDATA_NAME));
                    frapStudy.saveROIsAsExternalData(localWorkspace, frapStudy.getRoiExternalDataInfo().getExternalDataIdentifier(), frapStudy.getStartingIndexForRecovery());
                    frapStudy.saveImageDatasetAsExternalData(localWorkspace, frapStudy.getFrapDataExternalDataInfo().getExternalDataIdentifier(), frapStudy.getStartingIndexForRecovery());
                }
                // run ref sim
                frapStudy.setFrapOptData(new FRAPOptData(frapStudy, FRAPModel.NUM_MODEL_PARAMETERS_ONE_DIFF, localWorkspace, ctss));
            }
            FRAPOptData optData = frapStudy.getFrapOptData();
            // create frapModels
            if (frapStudy.getModels()[FRAPModel.IDX_MODEL_DIFF_ONE_COMPONENT] == null) {
                frapStudy.getModels()[FRAPModel.IDX_MODEL_DIFF_ONE_COMPONENT] = new FRAPModel(FRAPModel.MODEL_TYPE_ARRAY[FRAPModel.IDX_MODEL_DIFF_ONE_COMPONENT], null, null, null);
                if (frapStudy.getModels()[FRAPModel.IDX_MODEL_DIFF_ONE_COMPONENT].getModelParameters() == null) {
                    frapStudy.getFrapOptData().setNumEstimatedParams(FRAPModel.NUM_MODEL_PARAMETERS_ONE_DIFF);
                    Parameter[] initialParams = FRAPModel.getInitialParameters(frapStudy.getFrapData(), FRAPModel.MODEL_TYPE_ARRAY[FRAPModel.IDX_MODEL_DIFF_ONE_COMPONENT], frapStudy.getStartingIndexForRecovery());
                    Parameter[] bestParameters = frapStudy.getFrapOptData().getBestParamters(initialParams, frapStudy.getSelectedROIsForErrorCalculation());
                    frapStudy.getModels()[FRAPModel.IDX_MODEL_DIFF_ONE_COMPONENT].setModelParameters(bestParameters);
                }
            }
            if (frapStudy.getModels()[FRAPModel.IDX_MODEL_DIFF_TWO_COMPONENTS] == null) {
                frapStudy.getModels()[FRAPModel.IDX_MODEL_DIFF_TWO_COMPONENTS] = new FRAPModel(FRAPModel.MODEL_TYPE_ARRAY[FRAPModel.IDX_MODEL_DIFF_TWO_COMPONENTS], null, null, null);
                if (frapStudy.getModels()[FRAPModel.IDX_MODEL_DIFF_TWO_COMPONENTS].getModelParameters() == null) {
                    frapStudy.getFrapOptData().setNumEstimatedParams(FRAPModel.NUM_MODEL_PARAMETERS_TWO_DIFF);
                    Parameter[] initialParams = FRAPModel.getInitialParameters(frapStudy.getFrapData(), FRAPModel.MODEL_TYPE_ARRAY[FRAPModel.IDX_MODEL_DIFF_TWO_COMPONENTS], frapStudy.getStartingIndexForRecovery());
                    Parameter[] bestParameters = frapStudy.getFrapOptData().getBestParamters(initialParams, frapStudy.getSelectedROIsForErrorCalculation());
                    frapStudy.getModels()[FRAPModel.IDX_MODEL_DIFF_TWO_COMPONENTS].setModelParameters(bestParameters);
                }
            }
            // try diffusion with one diffusing component model
            System.out.println("Evaluating parameters in diffusion with one diffusing compoent model...");
            Parameter[] bestParameters = frapStudy.getModels()[FRAPModel.IDX_MODEL_DIFF_ONE_COMPONENT].getModelParameters();
            ProfileData[] profileData = optData.evaluateParameters(bestParameters, ctss);
            // output profile likelihood
            File outputDir_oneComponent = new File(getLocalWorkspace().getDefaultWorkspaceDirectory() + SUB_DIRECTORY + "OneComponent_SAVED_AT_" + BeanUtils.generateDateTimeString() + System.getProperty("file.separator"));
            if (!outputDir_oneComponent.exists()) {
                outputDir_oneComponent.mkdirs();
            }
            for (int i = 0; i < profileData.length; i++) {
                ProfileDataElement profileDataElement = profileData[i].getProfileDataElements().get(0);
                outputProfileLikelihood(profileData[i].getProfileDataElements(), profileDataElement.getParamName(), outputDir_oneComponent);
            }
            // try diffusion with two diffusing components model
            System.out.println("Evaluating parameters in diffusion with two diffusing compoents model...");
            bestParameters = frapStudy.getModels()[FRAPModel.IDX_MODEL_DIFF_TWO_COMPONENTS].getModelParameters();
            profileData = optData.evaluateParameters(bestParameters, ctss);
            // output profile likelihood
            File outputDir_twoComponents = new File(getLocalWorkspace().getDefaultWorkspaceDirectory() + SUB_DIRECTORY + "TwoComponents_SAVED_AT_" + BeanUtils.generateDateTimeString() + System.getProperty("file.separator"));
            if (!outputDir_twoComponents.exists()) {
                outputDir_twoComponents.mkdirs();
            }
            for (int i = 0; i < profileData.length; i++) {
                ProfileDataElement profileDataElement = profileData[i].getProfileDataElements().get(0);
                outputProfileLikelihood(profileData[i].getProfileDataElements(), profileDataElement.getParamName(), outputDir_twoComponents);
            }
        } catch (Exception e) {
            e.printStackTrace(System.out);
            System.exit(1);
        }
    }
}
Also used : ProfileData(org.vcell.optimization.ProfileData) Point(java.awt.Point) IOException(java.io.IOException) ProgressDialogListener(org.vcell.util.ProgressDialogListener) ProfileDataElement(org.vcell.optimization.ProfileDataElement) ClientTaskStatusSupport(org.vcell.util.ClientTaskStatusSupport) Parameter(cbit.vcell.opt.Parameter) File(java.io.File)

Aggregations

ProfileData (org.vcell.optimization.ProfileData)18 Parameter (cbit.vcell.opt.Parameter)6 ProfileDataElement (org.vcell.optimization.ProfileDataElement)6 AsynchClientTask (cbit.vcell.client.task.AsynchClientTask)5 Dimension (java.awt.Dimension)5 Hashtable (java.util.Hashtable)5 RowColumnResultSet (cbit.vcell.math.RowColumnResultSet)4 BoxLayout (javax.swing.BoxLayout)4 JPanel (javax.swing.JPanel)4 JScrollPane (javax.swing.JScrollPane)4 EtchedBorder (javax.swing.border.EtchedBorder)4 ConfidenceIntervalPlotPanel (org.vcell.optimization.gui.ConfidenceIntervalPlotPanel)4 ProfileDataPanel (org.vcell.optimization.gui.ProfileDataPanel)4 ROI (cbit.vcell.VirtualMicroscopy.ROI)3 WindowListener (java.awt.event.WindowListener)3 Image (cbit.vcell.VirtualMicroscopy.Image)2 FRAPOptData (cbit.vcell.microscopy.FRAPOptData)2 FRAPOptFunctions (cbit.vcell.microscopy.FRAPOptFunctions)2 FRAPStudy (cbit.vcell.microscopy.FRAPStudy)2 Point (java.awt.Point)2
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