Examples with Feature net.sf.mzmine.datamodel.Feature used on opensource projects

Example 61 with Feature

use of net.sf.mzmine.datamodel.Feature in project mzmine2 by mzmine.

the class PeakListRowLearnerTask method run.

/**
 * @see Runnable#run()
 */
@Override
public void run() {
    setStatus(TaskStatus.PROCESSING);
    logger.info("Running learner task on " + peakList);
    // Create a new results peakList which is added at the end
    resultPeakList = new SimplePeakList(peakList + " " + suffix, peakList.getRawDataFiles());
    /**
     * - A PeakList is a list of Features (peak in retention time dimension with accurate m/z)<br>
     * ---- contains one or multiple RawDataFiles <br>
     * ---- access mean retention time, mean m/z, maximum intensity, ...<br>
     */
    // get all rows and sort by m/z
    PeakListRow[] rows = peakList.getRows();
    Arrays.sort(rows, new PeakListRowSorter(SortingProperty.MZ, SortingDirection.Ascending));
    totalRows = rows.length;
    for (int i = 0; i < totalRows; i++) {
        // check for cancelled state and stop
        if (isCanceled())
            return;
        PeakListRow row = rows[i];
        // access details
        double mz = row.getAverageMZ();
        double intensity = row.getAverageHeight();
        double rt = row.getAverageRT();
        Feature peak = row.getBestPeak();
        // do stuff
        // ...
        // add row to peaklist result
        PeakListRow copy = copyPeakRow(row);
        resultPeakList.addRow(copy);
        // Update completion rate
        processedPeaks++;
    }
    // add to project
    addResultToProject();
    logger.info("Finished on " + peakList);
    setStatus(TaskStatus.FINISHED);
}

Example 62 with Feature

use of net.sf.mzmine.datamodel.Feature in project mzmine2 by mzmine.

the class HeatMapTask method modifySimpleDataset.

private double[][] modifySimpleDataset(UserParameter<?, ?> selectedParameter, String referenceGroup) {
    // Collect all data files
    Vector<RawDataFile> allDataFiles = new Vector<RawDataFile>();
    allDataFiles.addAll(Arrays.asList(peakList.getRawDataFiles()));
    // Determine the reference group and non reference group (the rest of
    // the samples) for raw data files
    List<RawDataFile> referenceDataFiles = new ArrayList<RawDataFile>();
    List<RawDataFile> nonReferenceDataFiles = new ArrayList<RawDataFile>();
    for (RawDataFile rawDataFile : allDataFiles) {
        Object paramValue = project.getParameterValue(selectedParameter, rawDataFile);
        if (paramValue.equals(referenceGroup)) {
            referenceDataFiles.add(rawDataFile);
        } else {
            nonReferenceDataFiles.add(rawDataFile);
        }
    }
    int numRows = 0;
    for (int row = 0; row < peakList.getNumberOfRows(); row++) {
        if (!onlyIdentified || (onlyIdentified && peakList.getRow(row).getPeakIdentities().length > 0)) {
            numRows++;
        }
    }
    // Create a new aligned feature list with all the samples if the reference
    // group has to be shown or with only
    // the non reference group if not.
    double[][] dataMatrix;
    if (rcontrol) {
        dataMatrix = new double[allDataFiles.size()][numRows];
    } else {
        dataMatrix = new double[nonReferenceDataFiles.size()][numRows];
    }
    // Data files that should be in the heat map
    List<RawDataFile> shownDataFiles = null;
    if (rcontrol) {
        shownDataFiles = allDataFiles;
    } else {
        shownDataFiles = nonReferenceDataFiles;
    }
    for (int row = 0, rowIndex = 0; row < peakList.getNumberOfRows(); row++) {
        PeakListRow rowPeak = peakList.getRow(row);
        if (!onlyIdentified || (onlyIdentified && rowPeak.getPeakIdentities().length > 0)) {
            // Average area or height of the reference group
            double referenceAverage = 0;
            int referencePeakCount = 0;
            for (int column = 0; column < referenceDataFiles.size(); column++) {
                if (rowPeak.getPeak(referenceDataFiles.get(column)) != null) {
                    if (area) {
                        referenceAverage += rowPeak.getPeak(referenceDataFiles.get(column)).getArea();
                    } else {
                        referenceAverage += rowPeak.getPeak(referenceDataFiles.get(column)).getHeight();
                    }
                    referencePeakCount++;
                }
            }
            if (referencePeakCount > 0) {
                referenceAverage /= referencePeakCount;
            }
            // area or height of the reference peaks in each row
            for (int column = 0; column < shownDataFiles.size(); column++) {
                double value = Double.NaN;
                if (rowPeak.getPeak(shownDataFiles.get(column)) != null) {
                    Feature peak = rowPeak.getPeak(shownDataFiles.get(column));
                    if (area) {
                        value = peak.getArea() / referenceAverage;
                    } else {
                        value = peak.getHeight() / referenceAverage;
                    }
                    if (log) {
                        value = Math.log(value);
                    }
                }
                dataMatrix[column][rowIndex] = value;
            }
            rowIndex++;
        }
    }
    // deviation of each column
    if (scale) {
        scale(dataMatrix);
    }
    // Create two arrays: row and column names
    rowNames = new String[dataMatrix[0].length];
    colNames = new String[shownDataFiles.size()];
    for (int column = 0; column < shownDataFiles.size(); column++) {
        colNames[column] = shownDataFiles.get(column).getName();
    }
    for (int row = 0, rowIndex = 0; row < peakList.getNumberOfRows(); row++) {
        if (!onlyIdentified || (onlyIdentified && peakList.getRow(row).getPeakIdentities().length > 0)) {
            if (peakList.getRow(row).getPeakIdentities() != null && peakList.getRow(row).getPeakIdentities().length > 0) {
                rowNames[rowIndex++] = peakList.getRow(row).getPreferredPeakIdentity().getName();
            } else {
                rowNames[rowIndex++] = "Unknown";
            }
        }
    }
    return dataMatrix;
}

Example 63 with Feature

use of net.sf.mzmine.datamodel.Feature in project mzmine2 by mzmine.

the class PeakLearnerTask method copyPeakRow.

/**
 * Create a copy of a feature list row.
 *
 * @param row the row to copy.
 * @return the newly created copy.
 */
private static PeakListRow copyPeakRow(final PeakListRow row) {
    // Copy the feature list row.
    final PeakListRow newRow = new SimplePeakListRow(row.getID());
    PeakUtils.copyPeakListRowProperties(row, newRow);
    // Copy the peaks.
    for (final Feature peak : row.getPeaks()) {
        final Feature newPeak = new SimpleFeature(peak);
        PeakUtils.copyPeakProperties(peak, newPeak);
        newRow.addPeak(peak.getDataFile(), newPeak);
    }
    return newRow;
}

Example 64 with Feature

use of net.sf.mzmine.datamodel.Feature in project mzmine2 by mzmine.

the class StreamPeakListRowLearnerTask method run.

/**
 * @see Runnable#run()
 */
@Override
public void run() {
    setStatus(TaskStatus.PROCESSING);
    logger.info("Running learner task on " + peakList);
    // Create a new results peakList which is added at the end
    resultPeakList = new SimplePeakList(peakList + " " + suffix, peakList.getRawDataFiles());
    /**
     * - A PeakList is a list of Features (peak in retention time dimension with accurate m/z)<br>
     * ---- contains one or multiple RawDataFiles <br>
     * ---- access mean retention time, mean m/z, maximum intensity, ...<br>
     */
    // use streams to filter, sort and create list
    List<PeakListRow> rowList = Arrays.stream(peakList.getRows()).filter(r -> r.getAverageHeight() > 5000).sorted(new PeakListRowSorter(SortingProperty.MZ, SortingDirection.Ascending)).collect(Collectors.toList());
    totalRows = rowList.size();
    // ###########################################################
    // OPTION 1: Streams
    // either use stream to process all rows
    rowList.stream().forEachOrdered(row -> {
        // check for cancelled state and stop
        if (isCanceled())
            return;
        // access details
        double mz = row.getAverageMZ();
        double intensity = row.getAverageHeight();
        double rt = row.getAverageRT();
        Feature peak = row.getBestPeak();
        // do stuff
        // ...
        // add row to peaklist result
        PeakListRow copy = copyPeakRow(row);
        resultPeakList.addRow(copy);
        // Update completion rate
        processedPeaks++;
    });
    // OPTION 2: For loop
    for (PeakListRow row : rowList) {
        // check for cancelled state and stop
        if (isCanceled())
            return;
        // access details
        double mz = row.getAverageMZ();
        double intensity = row.getAverageHeight();
        double rt = row.getAverageRT();
        Feature peak = row.getBestPeak();
        // do stuff
        // ...
        // add row to peaklist result
        PeakListRow copy = copyPeakRow(row);
        resultPeakList.addRow(copy);
        // Update completion rate
        processedPeaks++;
    }
    // add to project
    addResultToProject();
    logger.info("Finished on " + peakList);
    setStatus(TaskStatus.FINISHED);
}

Example 65 with Feature

use of net.sf.mzmine.datamodel.Feature in project mzmine2 by mzmine.

the class CDADataset method run.

@Override
public void run() {
    status = TaskStatus.PROCESSING;
    if (selectedRows.length == 0) {
        this.status = TaskStatus.ERROR;
        errorMessage = "No peaks selected for CDA plot";
        return;
    }
    if (selectedRawDataFiles.length == 0) {
        this.status = TaskStatus.ERROR;
        errorMessage = "No raw data files selected for CDA plot";
        return;
    }
    logger.info("Computing projection plot");
    // Generate matrix of raw data (input to CDA)
    boolean useArea = false;
    if (parameters.getParameter(ProjectionPlotParameters.peakMeasurementType).getValue() == PeakMeasurementType.AREA)
        useArea = true;
    double[][] rawData = new double[selectedRawDataFiles.length][selectedRows.length];
    for (int rowIndex = 0; rowIndex < selectedRows.length; rowIndex++) {
        PeakListRow peakListRow = selectedRows[rowIndex];
        for (int fileIndex = 0; fileIndex < selectedRawDataFiles.length; fileIndex++) {
            RawDataFile rawDataFile = selectedRawDataFiles[fileIndex];
            Feature p = peakListRow.getPeak(rawDataFile);
            if (p != null) {
                if (useArea)
                    rawData[fileIndex][rowIndex] = p.getArea();
                else
                    rawData[fileIndex][rowIndex] = p.getHeight();
            }
        }
    }
    int numComponents = xAxisDimension;
    if (yAxisDimension > numComponents)
        numComponents = yAxisDimension;
    // Scale data and do CDA
    Preprocess.scaleToUnityVariance(rawData);
    CDA cdaProj = new CDA(rawData);
    cdaProj.iterate(100);
    if (status == TaskStatus.CANCELED)
        return;
    double[][] result = cdaProj.getState();
    if (status == TaskStatus.CANCELED)
        return;
    component1Coords = result[xAxisDimension - 1];
    component2Coords = result[yAxisDimension - 1];
    ProjectionPlotWindow newFrame = new ProjectionPlotWindow(peakList, this, parameters);
    newFrame.setVisible(true);
    status = TaskStatus.FINISHED;
    logger.info("Finished computing projection plot.");
}