Examples with Plot2D cbit.plot.Plot2D used on opensource projects

Example 21 with Plot2D

use of cbit.plot.Plot2D in project vcell by virtualcell.

the class Plot2DPanel method getSamplePlot2D.

public static Plot2D getSamplePlot2D() {
    PlotData plotData1 = getSamplePlotData();
    int size = 30;
    double offset = 20;
    double[] xArray = new double[size];
    double[] yArray = new double[size];
    double w = 4 * Math.PI / size;
    for (int i = 0; i < size; i++) {
        xArray[i] = w * i;
        yArray[i] = 5 * i - offset;
    }
    PlotData plotData2 = new PlotData(xArray, yArray);
    xArray = new double[size];
    yArray = new double[size];
    for (int i = 0; i < size; i++) {
        xArray[i] = w * i;
        yArray[i] = 300 - Math.pow(i - offset, 2);
    }
    PlotData plotData3 = new PlotData(xArray, yArray);
    return new Plot2D(null, null, new String[] { "plot one", "plot two", "plot three" }, new PlotData[] { plotData1, plotData2, plotData3 }, new String[] { "title", "X Data", "Y Data" }, new boolean[] { true, false, true });
}

Example 22 with Plot2D

use of cbit.plot.Plot2D in project vcell by virtualcell.

the class Plot2DPanel method setPlot2D.

/**
 * Sets the plot2D property (cbit.plot.Plot2D) value.
 * @param plot2D The new value for the property.
 * @see #getPlot2D
 */
public void setPlot2D(Plot2D plot2D) {
    Plot2D oldValue = fieldPlot2D;
    fieldPlot2D = plot2D;
    firePropertyChange("plot2D", oldValue, plot2D);
}

Example 23 with Plot2D

use of cbit.plot.Plot2D 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 24 with Plot2D

use of cbit.plot.Plot2D 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;
}