Example 1 with ProfileDataElement
use of org.vcell.optimization.ProfileDataElement in project vcell by virtualcell.
the class MicroscopyXmlproducer method getXML.
private static Element getXML(ProfileData profileData) {
Element profileDataNode = new Element(MicroscopyXMLTags.ProfileDataTag);
ArrayList<ProfileDataElement> profileDataElements = profileData.getProfileDataElements();
for (int i = 0; i < profileDataElements.size(); i++) {
profileDataNode.addContent(getXML(profileDataElements.get(i)));
}
return profileDataNode;
}
Also used :
ProfileDataElement(org.vcell.optimization.ProfileDataElement)
ProfileDataElement(org.vcell.optimization.ProfileDataElement)
Element(org.jdom.Element)
Example 2 with ProfileDataElement
use of org.vcell.optimization.ProfileDataElement in project vcell by virtualcell.
the class FRAPOptFunctions method evaluateParameters.
// profileData array contains only two profile distribution, one for bleachWhileMonitoringRate and another for reaction off rate
public ProfileData[] evaluateParameters(Parameter[] currentParams, ClientTaskStatusSupport clientTaskStatusSupport) throws Exception {
int totalParamLen = currentParams.length;
int resultDataCounter = 0;
ProfileData[] resultData = new ProfileData[NUM_PARAM_ESTIMATED];
FRAPStudy frapStudy = getExpFrapStudy();
for (int j = 0; j < totalParamLen; j++) {
// only bleach while monitoring rate and reaction off rate need to evaluated
if (currentParams[j] != null && (currentParams[j].getName().equals(FRAPModel.MODEL_PARAMETER_NAMES[FRAPModel.INDEX_BLEACH_MONITOR_RATE]) || currentParams[j].getName().equals(FRAPModel.MODEL_PARAMETER_NAMES[FRAPModel.INDEX_OFF_RATE]))) {
ProfileData profileData = new ProfileData();
// add the fixed parameter to profileData, output exp data and opt results
Parameter[] newBestParameters = getBestParamters(frapStudy.getFrapData(), null, true);
double iniError = getOffRateResults().getObjectiveFunctionValue();
// fixed parameter(make sure parameter shall not be smaller than epsilon)
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) {
clientTaskStatusSupport.setMessage("<html>Evaluating confidence intervals of \'" + fixedParam.getName() + "\' <br> of finding reaction off rate 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);
// increase
int iterationCount = 1;
double paramLogVal = Math.log10(fixedParam.getInitialGuess());
double lastError = iniError;
boolean isBoundReached = false;
double incrementStep = FRAPOptData.DEFAULT_CI_STEPS_OFF_RATE[resultDataCounter];
int stepIncreaseCount = 0;
while (true) {
if (// if exceeds the maximum iterations, break;
iterationCount > FRAPOptData.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
Parameter[] newParameters = getBestParamters(frapStudy.getFrapData(), increasedParam, true);
double error = getOffRateResults().getObjectiveFunctionValue();
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) < FRAPOptData.MIN_LIKELIHOOD_CHANGE) {
stepIncreaseCount++;
incrementStep = FRAPOptData.DEFAULT_CI_STEPS_OFF_RATE[resultDataCounter] * Math.pow(2, stepIncreaseCount);
} else {
if (stepIncreaseCount > 1) {
incrementStep = FRAPOptData.DEFAULT_CI_STEPS_OFF_RATE[resultDataCounter] / Math.pow(2, stepIncreaseCount);
stepIncreaseCount--;
}
}
if (clientTaskStatusSupport.isInterrupted()) {
throw UserCancelException.CANCEL_GENERIC;
}
lastError = error;
iterationCount++;
clientTaskStatusSupport.setProgress((int) ((iterationCount * 1.0 / FRAPOptData.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 = FRAPOptData.DEFAULT_CI_STEPS_OFF_RATE[resultDataCounter];
stepIncreaseCount = 0;
while (true) {
if (// if exceeds the maximum iterations, break;
iterationCount > FRAPOptData.MAX_ITERATION) {
break;
}
if (isBoundReached) {
break;
}
paramLogVal = paramLogVal - decrementStep;
double paramVal = Math.pow(10, paramLogVal);
// System.out.println("paramVal:" + paramVal);
if (paramVal < (fixedParam.getLowerBound() + FRAPOptimizationUtils.epsilon)) {
paramVal = fixedParam.getLowerBound();
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
Parameter[] newParameters = getBestParamters(frapStudy.getFrapData(), decreasedParam, true);
double error = getOffRateResults().getObjectiveFunctionValue();
pde = new ProfileDataElement(decreasedParam.getName(), paramLogVal, error, newParameters);
profileData.addElement(0, pde);
if (error > (iniError + 10)) {
break;
}
if (Math.abs((error - lastError) / lastError) < FRAPOptData.MIN_LIKELIHOOD_CHANGE) {
stepIncreaseCount++;
decrementStep = FRAPOptData.DEFAULT_CI_STEPS_OFF_RATE[resultDataCounter] * Math.pow(2, stepIncreaseCount);
} else {
if (stepIncreaseCount > 1) {
incrementStep = FRAPOptData.DEFAULT_CI_STEPS_OFF_RATE[resultDataCounter] / Math.pow(2, stepIncreaseCount);
stepIncreaseCount--;
}
}
if (clientTaskStatusSupport.isInterrupted()) {
throw UserCancelException.CANCEL_GENERIC;
}
lastError = error;
iterationCount++;
clientTaskStatusSupport.setProgress((int) (((iterationCount + FRAPOptData.MAX_ITERATION) * 1.0 / FRAPOptData.MAX_ITERATION) * 0.5 * 100));
}
resultData[resultDataCounter++] = profileData;
// finish evaluation of a parameter
clientTaskStatusSupport.setProgress(100);
} else {
continue;
}
}
// 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 ...");
return resultData;
}
Also used :
ProfileDataElement(org.vcell.optimization.ProfileDataElement)
Parameter(cbit.vcell.opt.Parameter)
ProfileData(org.vcell.optimization.ProfileData)
Example 3 with ProfileDataElement
use of org.vcell.optimization.ProfileDataElement in project vcell by virtualcell.
the class MicroscopyXmlReader method getProfileData.
private ProfileData getProfileData(Element profileDataElement) {
ProfileData profileData = null;
if (profileDataElement != null) {
profileData = new ProfileData();
@SuppressWarnings("unchecked") List<Element> profileDataElementList = profileDataElement.getChildren(MicroscopyXMLTags.ProfieDataElementTag);
for (// loop through each profile data element
int i = 0; // loop through each profile data element
i < profileDataElementList.size(); // loop through each profile data element
i++) {
profileData.addElement(getProfileDataElement(profileDataElementList.get(i)));
}
}
return profileData;
}
Also used :
ProfileDataElement(org.vcell.optimization.ProfileDataElement)
Element(org.jdom.Element)
ProfileData(org.vcell.optimization.ProfileData)
Example 4 with ProfileDataElement
use of org.vcell.optimization.ProfileDataElement 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;
}
Also used :
ProfileDataElement(org.vcell.optimization.ProfileDataElement)
Parameter(cbit.vcell.opt.Parameter)
ProfileData(org.vcell.optimization.ProfileData)
Example 5 with ProfileDataElement
use of org.vcell.optimization.ProfileDataElement 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
ProfileDataElement (org.vcell.optimization.ProfileDataElement)10
Parameter (cbit.vcell.opt.Parameter)8
ProfileData (org.vcell.optimization.ProfileData)5
Element (org.jdom.Element)4
Plot2D (cbit.plot.Plot2D)2
PlotData (cbit.plot.PlotData)2
ConfidenceInterval (org.vcell.optimization.ConfidenceInterval)2
ProfileSummaryData (org.vcell.optimization.ProfileSummaryData)2
DescriptiveStatistics (org.vcell.util.DescriptiveStatistics)2
Point (java.awt.Point)1
File (java.io.File)1
IOException (java.io.IOException)1
ClientTaskStatusSupport (org.vcell.util.ClientTaskStatusSupport)1
ProgressDialogListener (org.vcell.util.ProgressDialogListener)1
