Examples of org.geotools.coverage.Category

org.geotools.coverage.Category
A category delimited by a range of sample values. A categogy may be either qualitative or quantitative. For exemple, a classified image may have a qualitative category defining sample value {@code 0}as water. An other qualitative category may defines sample value {@code 1}as forest, etc. An other image may define elevation data as sample values in the range {@code [0..100]}. The later is a quantitative category, because sample values are related to some measurement in the real world. For example, elevation data may be related to an altitude in metres through the following linear relation: altitude = sample value×100. Some image mixes both qualitative and quantitative categories. For example, images of Sea Surface Temperature (SST) may have a quantitative category for temperature with values ranging from 2 to 35°C, and three qualitative categories for cloud, land and ice.
All categories must have a human readable name. In addition, quantitative categories may define a transformation between sample values s and geophysics values x. This transformation is usually (but not always) a linear equation of the form:
x = {@linkplain GridSampleDimension#getOffset() offset} + {@linkplain GridSampleDimension#getScale() scale}×s
More general equation are allowed. For example, SeaWiFS images use a logarithmic transform. General transformations are expressed with a {@link MathTransform1D} object. In the special case where the transformationis a linear one (as in the formula above), then a {@code Category} objectmay be understood as the interval between two breakpoints in the JAI's {@linkplain javax.media.jai.operator.PiecewiseDescriptor piecewise} operation.
All {@code Category} objects are immutable and thread-safe. @since 2.1 @source $URL$ @version $Id$ @author Martin Desruisseaux (IRD) @see GridSampleDimension @see javax.media.jai.operator.PiecewiseDescriptor

            final List<Category> categories = sd[i].getCategories();
            if (categories != null && categories.size() >= 1) {
        writer.write("<nullValues>\n");
        for (Iterator<Category> it = sd[i].getCategories().iterator(); it
            .hasNext();) {
          Category cat = (Category) it.next();
          if ((cat != null)
              && cat.getName().toString().equalsIgnoreCase(
                  "no data")) {
            double min = cat.getRange().getMinimum();
            double max = cat.getRange().getMaximum();
            writer.write("<value>" + min + "</value>\n");
            if (min != max)
              writer.write("<value>" + max + "</value>\n");
          }
        }

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            final Color[] colorRange = null;


            final boolean geophysics = isGeoPhysics();


            Category valuesCat = new Category("values", colorRange, sampleValueRange,
                    LinearTransform1D.IDENTITY).geophysics(geophysics);


            Category[] categories;
            if (geophysics) {
                double noDataValue = band.getNoDataValue().doubleValue();
                // same as Category.NODATA but for the actual nodata value instead of hardcoded to
                // zero
                Category nodataCat = new Category(
                        Vocabulary.formatInternational(VocabularyKeys.NODATA), transparent,
                        noDataValue);
                categories = new Category[] { valuesCat, nodataCat };
            } else {
                // do not build a nodata category. A nodata value that doesn't overlap the value

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        NumberRange sourceRange = TypeMap.getRange(sourceType);
        for (int b=0; b<numBands; b++) {
            final Color[] c = colors!=null ? colors[b] : null;
            if (needScaling) {
                sourceRange = NumberRange.create(min[b], max[b]).castTo(sourceRange.getElementClass());
                categories[0] = new Category(n, c, targetRange, sourceRange);
            } else {
                categories[0] = new Category(n, c, targetRange, LinearTransform1D.IDENTITY);
            }
            dst[b] = new GridSampleDimension(name,categories, units).geophysics(true);
        }
    }

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                float[]    targetBreakpoints = null;
                double        expectedSource = Double.NaN;
                double        expectedTarget = Double.NaN;
                int jbp = 0; // Break point index (vary with j)
                for (int j=0; j<numCategories; j++) {
                    final Category sourceCategory = sources.get(j);
                    final Category packedCategory = sourceCategory.geophysics(false);
                    MathTransform1D transform = packedCategory.getSampleToGeophysics();
                    final double offset, scale;
                    if (transform == null) {
                        /*
                         * A qualitative category was found. Those categories maps NaN values,
                         * which need the special processing performed by our "SampleTranscode"
                         * operation. However there is a few special cases where JAI operations
                         * could still fit:
                         *
                         * - In "packed to geophysics" transform, we can still use "Piecewise"
                         *   if the minimum and maximum target value are equals (usually NaN).
                         *
                         * - In "geophysics to packed" transform, we can still use "Rescale"
                         *   if the NaN value maps to 0.
                         */
                        if (toGeo) {
                            canRescale = false;
                            final NumberRange target = sourceCategory.geophysics(true).getRange();
                            offset = target.getMinimum();
                            if (Double.doubleToRawLongBits(offset) != Double.doubleToRawLongBits(target.getMaximum())) {
                                canPiecewise = false;
                                break testLinear;
                            }
                            scale = 0;
                        } else {
                            canPiecewise = false;
                            assert !packedCategory.equals(sourceCategory) : packedCategory;
                            final NumberRange range = packedCategory.getRange();
                            if (range.getMinimum(true) == 0 && range.getMaximum(true) == 0) {
                                assert isNaN(sourceCategory.getRange().getMinimum()) : sourceCategory;
                                conditional = true;
                                continue;
                            }

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            PlanarImage.wrapRenderedImage(RasterSymbolizerTest
                .getSynthetic(Double.NaN)),
            new GeneralEnvelope(new double[] { -90, -180 },
                new double[] { 90, 180 }),
            new GridSampleDimension[] { new GridSampleDimension(
                "sd", new Category[] { new Category("",
                    Color.BLACK, 0) }, null) }, null, null);
      }
    };
    this.testedObject2 = new BaseCoverageProcessingNode(1,
        SimpleInternationalString.wrap("fake node"),
        SimpleInternationalString.wrap("fake node")) {


      protected GridCoverage2D execute() {
        return CoverageFactoryFinder.getGridCoverageFactory(null).create(
            "name",
            PlanarImage.wrapRenderedImage(RasterSymbolizerTest
                .getSynthetic(Double.NaN)),
            new GeneralEnvelope(new double[] { -90, -180 },
                new double[] { 90, 180 }),
            new GridSampleDimension[] { new GridSampleDimension(
                "sd", new Category[] { new Category("",
                    Color.BLACK, 0) }, null) }, null, null);
      }
    };
  }

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        .create(
            "name",
            PlanarImage.wrapRenderedImage(getSynthetic(Double.NaN)),
            envelope,
            new GridSampleDimension[] { new GridSampleDimension(
                "sd", new Category[] { new Category("",
                    Color.BLACK, 0) }, null) }, null, null);




    RasterSymbolizer rs = extractRasterSymbolizer(sld);

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                                                "name",
                                                PlanarImage.wrapRenderedImage(getSynthetic(Double.NaN)),
                                                new GeneralEnvelope(new double[] { -90, -180 },
                                                                new double[] { 90, 180 }),
                                                new GridSampleDimension[] { new GridSampleDimension(
                                                                "sd", new Category[] { new Category("",
                                                                                Color.BLACK, 0) }, null) }, null, null);


        
                SubchainStyleVisitorCoverageProcessingAdapter rsh = new RasterSymbolizerHelper(gc, null);
                RasterSymbolizer rs = extractRasterSymbolizer(sld);


                // visit the RasterSymbolizer
                rsh.visit(rs);
                IndexColorModel icm1 = (IndexColorModel) ((GridCoverage2D)rsh.getOutput()).getRenderedImage().getColorModel();
                testRasterSymbolizerHelper(rsh);
                
                
                ////
                //
                // Test using StyleBuilder
                //
                ////
                // get a coverage
                gc = CoverageFactoryFinder
                                .getGridCoverageFactory(null)
                                .create(
                                                "name",
                                                PlanarImage.wrapRenderedImage(getSynthetic(Double.NaN)),
                                                new GeneralEnvelope(new double[] { -90, -180 },
                                                                new double[] { 90, 180 }),
                                                new GridSampleDimension[] { new GridSampleDimension(
                                                                "sd", new Category[] { new Category("",
                                                                                Color.BLACK, 0) }, null) }, null, null);


                // build the RasterSymbolizer
                StyleBuilder sldBuilder = new StyleBuilder();
                rsh = new RasterSymbolizerHelper(gc, null);

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    // no data management
    final GridSampleDimension sd = (GridSampleDimension) gc
        .getSampleDimension(0);
    final List<Category> categories = sd.getCategories();
    final Iterator<Category> it = categories.iterator();
    Category candidate;
    double inNoData = Double.NaN;
    final String noDataName = Vocabulary.format(VocabularyKeys.NODATA);
    while (it.hasNext()) {
      candidate = (Category) it.next();
      final String name = candidate.getName().toString();
      if (name.equalsIgnoreCase("No Data")
          || name.equalsIgnoreCase(noDataName)) {
        inNoData = candidate.getRange().getMaximum();
      }
    }


    return inNoData;
  }

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    final GridCoverage2D gc = CoverageFactoryFinder.getGridCoverageFactory(null)
        .create(
            "name",
            PlanarImage.wrapRenderedImage(RasterSymbolizerTest.getSynthetic(Double.NaN)),
            new GeneralEnvelope(new double[] { -90, -180 },new double[] { 90, 180 }),
            new GridSampleDimension[] { new GridSampleDimension("sd", new Category[] { new Category("",Color.BLACK, 0) }, null) },
            null,
            null);


    root1 = new RootNode(gc);
    root2 = new RootNode(gc);

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      // Categories
      //
      //
      // //
      Unit<?> uom = null;
      final Category nan;
      if (Double.isNaN(inNoData)) {
        nan = new Category(Vocabulary
            .formatInternational(VocabularyKeys.NODATA), new Color(
            0, 0, 0, 0), 0);


      } else {
        nan = new Category(Vocabulary
            .formatInternational(VocabularyKeys.NODATA),
            new Color[] { new Color(0, 0, 0, 0) }, 
            NumberRange.create(0, 0),
            NumberRange.create(inNoData, inNoData));

View Full Code Here

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