@Ignore
@Test
public void testMakeNewMapWithParent() {
DoubleDataset rand = Random.rand(gridScanMap.getMap().getShape());
AxesMetadata ax = gridScanMap.getMap().getFirstMetadata(AxesMetadata.class);
MetadataType clone = ax.clone();
rand.setMetadata(clone);
MappedData map =
gridScanMap.makeNewMapWithParent("random", Random.rand(gridScanMap.getMap().getShape()));
assertEquals(gridScanBlock, map.getParent());
}
private static double[][] convertDoubleDataset2DtoPrimitive(DoubleDataset dataset) {
if (dataset.getRank() != 2) throw new IllegalArgumentException("dataset Shape must be 2D");
double[][] rv = new double[dataset.getShape()[0]][dataset.getShape()[1]];
for (int row = 0; row < dataset.getShape()[0]; row++) {
System.arraycopy(
dataset.getData(), row * dataset.getShape()[1], rv[row], 0, dataset.getShape()[1]);
}
return rv;
}
@Override
protected DataMessageComponent getTransformedMessage(List<DataMessageComponent> cache)
throws DataMessageException {
// get the data out of the message, name of the item should be specified
final Map<String, Serializable> data = MessageUtils.getList(cache);
Map<String, AFunction> functions;
try {
functions = MessageUtils.getFunctions(cache);
} catch (Exception e) {
throw new DataMessageException(
"Could not parse the Funcitons comming into the FunctionToDatsetActor", null, e);
}
// prepare the output message
DataMessageComponent result = MessageUtils.copy(cache);
// get the required datasets
String dataset = datasetName.getExpression();
String xAxis = xAxisName.getExpression();
String functionString = functionName.getExpression();
// Get the actual objects
Dataset xAxisDS = DatasetFactory.createFromObject(data.get(xAxis)).clone();
AFunction function = functions.get(functionString);
populateDataBasedFunctions(data, function);
// process the data
// TODO Add Null Protection here.
DoubleDataset createdDS = function.calculateValues(xAxisDS);
createdDS.setName(dataset);
// Add it to the result
result.addList(createdDS.getName(), createdDS);
return result;
}
@Override
protected OperationData process(IDataset input, IMonitor monitor) {
RectangularROI box = model.getBox();
Dataset in1 =
BoxSlicerRodScanUtils.rOIBox(input, monitor, box.getIntLengths(), box.getIntPoint());
if (g2 == null) g2 = new Polynomial2D(model.getFitPower());
if ((int) Math.pow(model.getFitPower() + 1, 2) != g2.getNoOfParameters())
g2 = new Polynomial2D(model.getFitPower());
Dataset[] fittingBackground =
BoxSlicerRodScanUtils.LeftRightTopBottomBoxes(
input, monitor, box.getIntLengths(), box.getIntPoint(), model.getBoundaryBox());
Dataset offset = DatasetFactory.ones(fittingBackground[2].getShape(), Dataset.FLOAT64);
Dataset intermediateFitTest = Maths.add(offset, fittingBackground[2]);
Dataset matrix =
LinearLeastSquaresServicesForSXRD.polynomial2DLinearLeastSquaresMatrixGenerator(
model.getFitPower(), fittingBackground[0], fittingBackground[1]);
DoubleDataset test = (DoubleDataset) LinearAlgebra.solveSVD(matrix, intermediateFitTest);
double[] params = test.getData();
DoubleDataset in1Background =
g2.getOutputValues0(
params, box.getIntLengths(), model.getBoundaryBox(), model.getFitPower());
IndexIterator it = in1Background.getIterator();
while (it.hasNext()) {
double v = in1Background.getElementDoubleAbs(it.index);
if (v < 0) in1Background.setObjectAbs(it.index, 0);
}
Dataset pBackgroundSubtracted = Maths.subtract(in1, in1Background, null);
pBackgroundSubtracted.setName("pBackgroundSubtracted");
IndexIterator it1 = pBackgroundSubtracted.getIterator();
while (it1.hasNext()) {
double q = pBackgroundSubtracted.getElementDoubleAbs(it1.index);
if (q < 0) pBackgroundSubtracted.setObjectAbs(it1.index, 0);
}
Dataset output = DatasetUtils.cast(pBackgroundSubtracted, Dataset.FLOAT64);
output.setName("Region of Interest, polynomial background removed");
return new OperationData(output);
}
/**
* Perform a two-dimensional interpolation
*
* @param oldx an IDataset containing a 1D array of X-values, sorted in increasing order,
* corresponding to the first dimension of <code>oldxy</code>
* @param oldy an IDataset containing a 1D array of Y-values, sorted in increasing order,
* corresponding to the second dimension of <code>oldxy</code>
* @param oldxy an IDataset containing a 2D grid of interpolation points
* @param newx an IDataset containing a 1D array of X-values that will be sent to the
* interpolating function
* @param newy an IDataset containing a 1D array of Y-values that will be sent to the
* interpolating function
* @param interpolator an instance of {@link
* org.apache.commons.math3.analysis.interpolation.BivariateGridInterpolator}
* @param output_type an {@link BicubicInterpolationOutput} that will determine how <code>newx
* </code> and <code>newy</code> will be interpreted, and therefore whether a 1D or 2D Dataset
* will be returned.
* @return rank 1 or 2 Dataset, depending on <code>output_type}</code>
* @throws NonMonotonicSequenceException
* @throws NumberIsTooSmallException
*/
public static Dataset interpolate(
IDataset oldx,
IDataset oldy,
IDataset oldxy,
IDataset newx,
IDataset newy,
BivariateGridInterpolator interpolator,
BicubicInterpolationOutput output_type)
throws NonMonotonicSequenceException, NumberIsTooSmallException {
// check shapes
if (oldx.getRank() != 1) throw new IllegalArgumentException("oldx Shape must be 1D");
if (oldy.getRank() != 1) throw new IllegalArgumentException("oldy Shape must be 1D");
if (oldxy.getRank() != 2) throw new IllegalArgumentException("oldxy Shape must be 2D");
if (oldx.getShape()[0] != oldxy.getShape()[0])
throw new IllegalArgumentException("oldx Shape must match oldxy Shape[0]");
if (oldy.getShape()[0] != oldxy.getShape()[1])
throw new IllegalArgumentException("oldy Shape must match oldxy Shape[1]");
if (newx.getRank() != 1) throw new IllegalArgumentException("newx Shape must be 1D");
if (newy.getRank() != 1) throw new IllegalArgumentException("newx Shape must be 1D");
if (output_type == BicubicInterpolationOutput.ONED && newy.getSize() != newx.getSize())
throw new IllegalArgumentException(
"newx and newy Size must be identical when expecting a rank 1 dataset result");
DoubleDataset oldx_dd = (DoubleDataset) DatasetUtils.cast(oldx, Dataset.FLOAT64);
DoubleDataset oldy_dd = (DoubleDataset) DatasetUtils.cast(oldy, Dataset.FLOAT64);
DoubleDataset oldxy_dd = (DoubleDataset) DatasetUtils.cast(oldxy, Dataset.FLOAT64);
// unlike in Interpolation1D, we will not be sorting here, as it just too complicated
// the user will be responsible for ensuring the arrays are properly sorted
// oldxy_dd needs to be transformed into a double[][] array
// this call may throw an exception that needs handling by the calling method
BivariateFunction func =
interpolator.interpolate(
oldx_dd.getData(), oldy_dd.getData(), convertDoubleDataset2DtoPrimitive(oldxy_dd));
Dataset rv = null;
if (output_type == BicubicInterpolationOutput.ONED) {
rv = DatasetFactory.zeros(new int[] {newx.getSize()}, Dataset.FLOAT64);
for (int i = 0; i < newx.getSize(); i++) {
double val = 0.0;
try {
val = func.value(newx.getDouble(i), newy.getDouble(i));
rv.set(val, i);
} catch (OutOfRangeException e) {
rv.set(0.0, i);
}
}
} else if (output_type == BicubicInterpolationOutput.TWOD) {
rv = DatasetFactory.zeros(new int[] {newx.getSize(), newy.getSize()}, Dataset.FLOAT64);
for (int i = 0; i < newx.getSize(); i++) {
for (int j = 0; j < newy.getSize(); j++) {
double val = 0.0;
try {
val = func.value(newx.getDouble(i), newy.getDouble(j));
rv.set(val, i, j);
} catch (OutOfRangeException e) {
rv.set(0.0, i, j);
}
}
}
}
rv.setName(oldxy.getName() + "_interpolated");
return rv;
}
