Examples with DescriptiveStatistics org.vcell.util.DescriptiveStatistics used on opensource projects

Example 1 with DescriptiveStatistics

use of org.vcell.util.DescriptiveStatistics in project vcell by virtualcell.

the class FRAPBatchRunWorkspace method refreshStatisticsData.

public void refreshStatisticsData() {
    int studySize = getFrapStudies().size();
    // get parameters array (column-name and column-details should be excluded)
    // parameterVals[0] primaryDiffRates, parameterVals[1] primaryMobiles,parameterVals[2]bwmRates
    // parameterVals[3] secDiffRates,parameterVals[4] secMobiles,parameterVals[5] bs_concentration
    // parameterVals[6] reaction on rate, parameterVals[7] reacton off rate, parameterVals[8] immobile
    double[][] parameterVals = new double[BatchRunResultsParamTableModel.NUM_COLUMNS - 2][studySize];
    for (int i = 0; i < (BatchRunResultsParamTableModel.NUM_COLUMNS - 2); i++) {
        // fill all elements with 1e8 first
        Arrays.fill(parameterVals[i], FRAPOptimizationUtils.largeNumber);
    }
    for (int i = 0; i < studySize; i++) {
        FRAPStudy fStudy = getFrapStudies().get(i);
        if (selectedModel == FRAPModel.IDX_MODEL_DIFF_ONE_COMPONENT) {
            FRAPModel fModel = fStudy.getModels()[FRAPModel.IDX_MODEL_DIFF_ONE_COMPONENT];
            parameterVals[0][i] = fModel.getModelParameters()[FRAPModel.INDEX_PRIMARY_DIFF_RATE].getInitialGuess();
            parameterVals[1][i] = fModel.getModelParameters()[FRAPModel.INDEX_PRIMARY_FRACTION].getInitialGuess();
            parameterVals[2][i] = fModel.getModelParameters()[FRAPModel.INDEX_BLEACH_MONITOR_RATE].getInitialGuess();
            // immobile fraction
            parameterVals[8][i] = Math.max(0, (1 - parameterVals[1][i]));
        } else if (selectedModel == FRAPModel.IDX_MODEL_DIFF_TWO_COMPONENTS) {
            FRAPModel fModel = fStudy.getModels()[FRAPModel.IDX_MODEL_DIFF_TWO_COMPONENTS];
            parameterVals[0][i] = fModel.getModelParameters()[FRAPModel.INDEX_PRIMARY_DIFF_RATE].getInitialGuess();
            parameterVals[1][i] = fModel.getModelParameters()[FRAPModel.INDEX_PRIMARY_FRACTION].getInitialGuess();
            parameterVals[2][i] = fModel.getModelParameters()[FRAPModel.INDEX_BLEACH_MONITOR_RATE].getInitialGuess();
            parameterVals[3][i] = fModel.getModelParameters()[FRAPModel.INDEX_SECONDARY_DIFF_RATE].getInitialGuess();
            parameterVals[4][i] = fModel.getModelParameters()[FRAPModel.INDEX_SECONDARY_FRACTION].getInitialGuess();
            // immobile fraction
            parameterVals[8][i] = Math.max(0, (1 - parameterVals[1][i] - parameterVals[4][i]));
        } else if (selectedModel == FRAPModel.IDX_MODEL_REACTION_OFF_RATE) {
            FRAPModel fModel = fStudy.getModels()[FRAPModel.IDX_MODEL_REACTION_OFF_RATE];
            parameterVals[2][i] = fModel.getModelParameters()[FRAPModel.INDEX_BLEACH_MONITOR_RATE].getInitialGuess();
            parameterVals[5][i] = fModel.getModelParameters()[FRAPModel.INDEX_BINDING_SITE_CONCENTRATION].getInitialGuess();
            parameterVals[7][i] = fModel.getModelParameters()[FRAPModel.INDEX_OFF_RATE].getInitialGuess();
        }
    }
    double[][] oldStatData = statisticsData;
    // stores statistics for each parameters . (column-name and column-details should be excluded)
    statisticsData = new double[BatchRunResultsStatTableModel.NUM_STATISTICS][BatchRunResultsParamTableModel.NUM_COLUMNS - 2];
    for (int i = 0; i < BatchRunResultsStatTableModel.NUM_STATISTICS; i++) {
        // fill all elements with 1e8 first
        Arrays.fill(statisticsData[i], FRAPOptimizationUtils.largeNumber);
    }
    for (int i = 0; i < BatchRunResultsParamTableModel.NUM_COLUMNS - 2; i++) {
        // statistics for parameters exist(which is not saved as 1e8)
        if (parameterVals[i][0] != FRAPOptimizationUtils.largeNumber) {
            DescriptiveStatistics stat = DescriptiveStatistics.CreateBasicStatistics(parameterVals[i]);
            statisticsData[BatchRunResultsStatTableModel.ROW_IDX_AVERAGE][i] = stat.getMean();
            statisticsData[BatchRunResultsStatTableModel.ROW_IDX_STD][i] = stat.getStandardDeviation();
            statisticsData[BatchRunResultsStatTableModel.ROW_IDX_MEDIAN][i] = stat.getMedian();
            statisticsData[BatchRunResultsStatTableModel.ROW_IDX_MIN][i] = stat.getMin();
            statisticsData[BatchRunResultsStatTableModel.ROW_IDX_MAX][i] = stat.getMax();
        }
    }
    updateAverageParameters();
    firePropertyChange(PROPERTY_CHANGE_BATCHRUN_UPDATE_STATISTICS, oldStatData, statisticsData);
}

Example 2 with DescriptiveStatistics

use of org.vcell.util.DescriptiveStatistics in project vcell by virtualcell.

the class FRAPOptimizationUtils method getSummaryFromProfileData.

// getting a profileSummary for each parameter that has acquired a profile likelihood distribution
public static ProfileSummaryData getSummaryFromProfileData(ProfileData profileData) {
    ArrayList<ProfileDataElement> profileElements = profileData.getProfileDataElements();
    int dataSize = profileElements.size();
    double[] paramValArray = new double[dataSize];
    double[] errorArray = new double[dataSize];
    if (dataSize > 0) {
        // profile likelihood curve
        String paramName = profileElements.get(0).getParamName();
        // find the parameter to locate the upper and lower bounds
        Parameter parameter = null;
        Parameter[] bestParameters = profileElements.get(0).getBestParameters();
        for (int i = 0; i < bestParameters.length; i++) {
            if (bestParameters[i] != null && bestParameters[i].getName().equals(paramName)) {
                parameter = bestParameters[i];
            }
        }
        // double logLowerBound = (lowerBound == 0)? 0: Math.log10(lowerBound);
        for (int i = 0; i < dataSize; i++) {
            paramValArray[i] = profileElements.get(i).getParameterValue();
            errorArray[i] = profileElements.get(i).getLikelihood();
        }
        PlotData dataPlot = new PlotData(paramValArray, errorArray);
        // get confidence interval line
        // make array copy in order to not change the data orders afte the sorting
        double[] paramValArrayCopy = new double[paramValArray.length];
        System.arraycopy(paramValArray, 0, paramValArrayCopy, 0, paramValArray.length);
        double[] errorArrayCopy = new double[errorArray.length];
        System.arraycopy(errorArray, 0, errorArrayCopy, 0, errorArray.length);
        DescriptiveStatistics paramValStat = DescriptiveStatistics.CreateBasicStatistics(paramValArrayCopy);
        DescriptiveStatistics errorStat = DescriptiveStatistics.CreateBasicStatistics(errorArrayCopy);
        double[] xArray = new double[2];
        double[][] yArray = new double[ConfidenceInterval.NUM_CONFIDENCE_LEVELS][2];
        // get confidence level plot lines
        xArray[0] = paramValStat.getMin() - (Math.abs(paramValStat.getMin()) * 0.2);
        xArray[1] = paramValStat.getMax() + (Math.abs(paramValStat.getMax()) * 0.2);
        for (int i = 0; i < ConfidenceInterval.NUM_CONFIDENCE_LEVELS; i++) {
            yArray[i][0] = errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i];
            yArray[i][1] = yArray[i][0];
        }
        PlotData confidence80Plot = new PlotData(xArray, yArray[ConfidenceInterval.IDX_DELTA_ALPHA_80]);
        PlotData confidence90Plot = new PlotData(xArray, yArray[ConfidenceInterval.IDX_DELTA_ALPHA_90]);
        PlotData confidence95Plot = new PlotData(xArray, yArray[ConfidenceInterval.IDX_DELTA_ALPHA_95]);
        PlotData confidence99Plot = new PlotData(xArray, yArray[ConfidenceInterval.IDX_DELTA_ALPHA_99]);
        // generate plot2D data
        Plot2D plots = new Plot2D(null, null, new String[] { "profile Likelihood Data", "80% confidence", "90% confidence", "95% confidence", "99% confidence" }, new PlotData[] { dataPlot, confidence80Plot, confidence90Plot, confidence95Plot, confidence99Plot }, new String[] { "Profile likelihood of " + paramName, "Log base 10 of " + paramName, "Profile Likelihood" }, new boolean[] { true, true, true, true, true });
        // get the best parameter for the minimal error
        int minErrIndex = -1;
        for (int i = 0; i < errorArray.length; i++) {
            if (errorArray[i] == errorStat.getMin()) {
                minErrIndex = i;
                break;
            }
        }
        double bestParamVal = Math.pow(10, paramValArray[minErrIndex]);
        // find confidence interval points
        ConfidenceInterval[] intervals = new ConfidenceInterval[ConfidenceInterval.NUM_CONFIDENCE_LEVELS];
        // half loop through the errors(left side curve)
        int[] smallLeftIdx = new int[ConfidenceInterval.NUM_CONFIDENCE_LEVELS];
        int[] bigLeftIdx = new int[ConfidenceInterval.NUM_CONFIDENCE_LEVELS];
        for (int i = 0; i < ConfidenceInterval.NUM_CONFIDENCE_LEVELS; i++) {
            smallLeftIdx[i] = -1;
            bigLeftIdx[i] = -1;
            for (// loop from bigger error to smaller error
            int j = 1; // loop from bigger error to smaller error
            j < minErrIndex + 1; // loop from bigger error to smaller error
            j++) {
                if ((errorArray[j] < (errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i])) && (errorArray[j - 1] > (errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i]))) {
                    smallLeftIdx[i] = j - 1;
                    bigLeftIdx[i] = j;
                    break;
                }
            }
        }
        // another half loop through the errors(right side curve)
        int[] smallRightIdx = new int[ConfidenceInterval.NUM_CONFIDENCE_LEVELS];
        int[] bigRightIdx = new int[ConfidenceInterval.NUM_CONFIDENCE_LEVELS];
        for (int i = 0; i < ConfidenceInterval.NUM_CONFIDENCE_LEVELS; i++) {
            smallRightIdx[i] = -1;
            bigRightIdx[i] = -1;
            for (// loop from bigger error to smaller error
            int j = (minErrIndex + 1); // loop from bigger error to smaller error
            j < errorArray.length; // loop from bigger error to smaller error
            j++) {
                if ((errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i]) < errorArray[j] && (errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i]) > errorArray[j - 1]) {
                    smallRightIdx[i] = j - 1;
                    bigRightIdx[i] = j;
                    break;
                }
            }
        }
        // calculate intervals
        for (int i = 0; i < ConfidenceInterval.NUM_CONFIDENCE_LEVELS; i++) {
            double lowerBound = Double.NEGATIVE_INFINITY;
            boolean bLowerBoundOpen = true;
            double upperBound = Double.POSITIVE_INFINITY;
            boolean bUpperBoundOpen = true;
            if (// no lower bound
            smallLeftIdx[i] == -1 && bigLeftIdx[i] == -1) {
                lowerBound = parameter.getLowerBound();
                bLowerBoundOpen = false;
            } else if (// there is a lower bound
            smallLeftIdx[i] != -1 && bigLeftIdx[i] != -1) {
                // x=x1+(x2-x1)*(y-y1)/(y2-y1);
                double x1 = paramValArray[smallLeftIdx[i]];
                double x2 = paramValArray[bigLeftIdx[i]];
                double y = errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i];
                double y1 = errorArray[smallLeftIdx[i]];
                double y2 = errorArray[bigLeftIdx[i]];
                lowerBound = x1 + (x2 - x1) * (y - y1) / (y2 - y1);
                lowerBound = Math.pow(10, lowerBound);
                bLowerBoundOpen = false;
            }
            if (// no upper bound
            smallRightIdx[i] == -1 && bigRightIdx[i] == -1) {
                upperBound = parameter.getUpperBound();
                bUpperBoundOpen = false;
            } else if (// there is a upper bound
            smallRightIdx[i] != -1 && bigRightIdx[i] != -1) {
                // x=x1+(x2-x1)*(y-y1)/(y2-y1);
                double x1 = paramValArray[smallRightIdx[i]];
                double x2 = paramValArray[bigRightIdx[i]];
                double y = errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i];
                double y1 = errorArray[smallRightIdx[i]];
                double y2 = errorArray[bigRightIdx[i]];
                upperBound = x1 + (x2 - x1) * (y - y1) / (y2 - y1);
                upperBound = Math.pow(10, upperBound);
                bUpperBoundOpen = false;
            }
            intervals[i] = new ConfidenceInterval(lowerBound, bLowerBoundOpen, upperBound, bUpperBoundOpen);
        }
        return new ProfileSummaryData(plots, bestParamVal, intervals, paramName);
    }
    return null;
}

Example 3 with DescriptiveStatistics

use of org.vcell.util.DescriptiveStatistics in project vcell by virtualcell.

the class DisplayProfileLikelihoodPlotsOp method getSummaryFromProfileData.

// getting a profileSummary for each parameter that has acquired a profile likelihood distribution
ProfileSummaryData getSummaryFromProfileData(ProfileData profileData) {
    ArrayList<ProfileDataElement> profileElements = profileData.getProfileDataElements();
    int dataSize = profileElements.size();
    double[] paramValArray = new double[dataSize];
    double[] errorArray = new double[dataSize];
    if (dataSize > 0) {
        // profile likelihood curve
        String paramName = profileElements.get(0).getParamName();
        // find the parameter to locate the upper and lower bounds
        Parameter parameter = null;
        Parameter[] bestParameters = profileElements.get(0).getBestParameters();
        for (int i = 0; i < bestParameters.length; i++) {
            if (bestParameters[i] != null && bestParameters[i].getName().equals(paramName)) {
                parameter = bestParameters[i];
            }
        }
        // double logLowerBound = (lowerBound == 0)? 0: Math.log10(lowerBound);
        for (int i = 0; i < dataSize; i++) {
            paramValArray[i] = profileElements.get(i).getParameterValue();
            errorArray[i] = profileElements.get(i).getLikelihood();
        }
        PlotData dataPlot = new PlotData(paramValArray, errorArray);
        // get confidence interval line
        // make array copy in order to not change the data orders afte the sorting
        double[] paramValArrayCopy = new double[paramValArray.length];
        System.arraycopy(paramValArray, 0, paramValArrayCopy, 0, paramValArray.length);
        double[] errorArrayCopy = new double[errorArray.length];
        System.arraycopy(errorArray, 0, errorArrayCopy, 0, errorArray.length);
        DescriptiveStatistics paramValStat = DescriptiveStatistics.CreateBasicStatistics(paramValArrayCopy);
        DescriptiveStatistics errorStat = DescriptiveStatistics.CreateBasicStatistics(errorArrayCopy);
        double[] xArray = new double[2];
        double[][] yArray = new double[ConfidenceInterval.NUM_CONFIDENCE_LEVELS][2];
        // get confidence level plot lines
        xArray[0] = paramValStat.getMin() - (Math.abs(paramValStat.getMin()) * 0.2);
        xArray[1] = paramValStat.getMax() + (Math.abs(paramValStat.getMax()) * 0.2);
        for (int i = 0; i < ConfidenceInterval.NUM_CONFIDENCE_LEVELS; i++) {
            yArray[i][0] = errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i];
            yArray[i][1] = yArray[i][0];
        }
        PlotData confidence80Plot = new PlotData(xArray, yArray[ConfidenceInterval.IDX_DELTA_ALPHA_80]);
        PlotData confidence90Plot = new PlotData(xArray, yArray[ConfidenceInterval.IDX_DELTA_ALPHA_90]);
        PlotData confidence95Plot = new PlotData(xArray, yArray[ConfidenceInterval.IDX_DELTA_ALPHA_95]);
        PlotData confidence99Plot = new PlotData(xArray, yArray[ConfidenceInterval.IDX_DELTA_ALPHA_99]);
        // generate plot2D data
        Plot2D plots = new Plot2D(null, null, new String[] { "profile Likelihood Data", "80% confidence", "90% confidence", "95% confidence", "99% confidence" }, new PlotData[] { dataPlot, confidence80Plot, confidence90Plot, confidence95Plot, confidence99Plot }, new String[] { "Profile likelihood of " + paramName, "Log base 10 of " + paramName, "Profile Likelihood" }, new boolean[] { true, true, true, true, true });
        // get the best parameter for the minimal error
        int minErrIndex = -1;
        for (int i = 0; i < errorArray.length; i++) {
            if (errorArray[i] == errorStat.getMin()) {
                minErrIndex = i;
                break;
            }
        }
        double bestParamVal = Math.pow(10, paramValArray[minErrIndex]);
        // find confidence interval points
        ConfidenceInterval[] intervals = new ConfidenceInterval[ConfidenceInterval.NUM_CONFIDENCE_LEVELS];
        // half loop through the errors(left side curve)
        int[] smallLeftIdx = new int[ConfidenceInterval.NUM_CONFIDENCE_LEVELS];
        int[] bigLeftIdx = new int[ConfidenceInterval.NUM_CONFIDENCE_LEVELS];
        for (int i = 0; i < ConfidenceInterval.NUM_CONFIDENCE_LEVELS; i++) {
            smallLeftIdx[i] = -1;
            bigLeftIdx[i] = -1;
            for (// loop from bigger error to smaller error
            int j = 1; // loop from bigger error to smaller error
            j < minErrIndex + 1; // loop from bigger error to smaller error
            j++) {
                if ((errorArray[j] < (errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i])) && (errorArray[j - 1] > (errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i]))) {
                    smallLeftIdx[i] = j - 1;
                    bigLeftIdx[i] = j;
                    break;
                }
            }
        }
        // another half loop through the errors(right side curve)
        int[] smallRightIdx = new int[ConfidenceInterval.NUM_CONFIDENCE_LEVELS];
        int[] bigRightIdx = new int[ConfidenceInterval.NUM_CONFIDENCE_LEVELS];
        for (int i = 0; i < ConfidenceInterval.NUM_CONFIDENCE_LEVELS; i++) {
            smallRightIdx[i] = -1;
            bigRightIdx[i] = -1;
            for (// loop from bigger error to smaller error
            int j = (minErrIndex + 1); // loop from bigger error to smaller error
            j < errorArray.length; // loop from bigger error to smaller error
            j++) {
                if ((errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i]) < errorArray[j] && (errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i]) > errorArray[j - 1]) {
                    smallRightIdx[i] = j - 1;
                    bigRightIdx[i] = j;
                    break;
                }
            }
        }
        // calculate intervals
        for (int i = 0; i < ConfidenceInterval.NUM_CONFIDENCE_LEVELS; i++) {
            double lowerBound = Double.NEGATIVE_INFINITY;
            boolean bLowerBoundOpen = true;
            double upperBound = Double.POSITIVE_INFINITY;
            boolean bUpperBoundOpen = true;
            if (// no lower bound
            smallLeftIdx[i] == -1 && bigLeftIdx[i] == -1) {
                lowerBound = parameter.getLowerBound();
                bLowerBoundOpen = false;
            } else if (// there is a lower bound
            smallLeftIdx[i] != -1 && bigLeftIdx[i] != -1) {
                // x=x1+(x2-x1)*(y-y1)/(y2-y1);
                double x1 = paramValArray[smallLeftIdx[i]];
                double x2 = paramValArray[bigLeftIdx[i]];
                double y = errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i];
                double y1 = errorArray[smallLeftIdx[i]];
                double y2 = errorArray[bigLeftIdx[i]];
                lowerBound = x1 + (x2 - x1) * (y - y1) / (y2 - y1);
                lowerBound = Math.pow(10, lowerBound);
                bLowerBoundOpen = false;
            }
            if (// no upper bound
            smallRightIdx[i] == -1 && bigRightIdx[i] == -1) {
                upperBound = parameter.getUpperBound();
                bUpperBoundOpen = false;
            } else if (// there is a upper bound
            smallRightIdx[i] != -1 && bigRightIdx[i] != -1) {
                // x=x1+(x2-x1)*(y-y1)/(y2-y1);
                double x1 = paramValArray[smallRightIdx[i]];
                double x2 = paramValArray[bigRightIdx[i]];
                double y = errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i];
                double y1 = errorArray[smallRightIdx[i]];
                double y2 = errorArray[bigRightIdx[i]];
                upperBound = x1 + (x2 - x1) * (y - y1) / (y2 - y1);
                upperBound = Math.pow(10, upperBound);
                bUpperBoundOpen = false;
            }
            intervals[i] = new ConfidenceInterval(lowerBound, bLowerBoundOpen, upperBound, bUpperBoundOpen);
        }
        return new ProfileSummaryData(plots, bestParamVal, intervals, paramName);
    }
    return null;
}