/**
* Element wise divide the array1 value by value from given array2.
*
* @param array IArray object
* @return this array1 after modification
* @throws ShapeNotMatchException mismatching shape
*/
public IArrayMath eltDivide(IArray newArray) throws ShapeNotMatchException {
getArray().getArrayUtils().checkShape(newArray);
if (getArray().getRank() == newArray.getRank()) {
eltDivideWithEqualSize(newArray, getArray());
} else {
ISliceIterator sourceSliceIterator = null;
try {
sourceSliceIterator = getArray().getSliceIterator(newArray.getRank());
while (sourceSliceIterator.hasNext()) {
IArray sourceSlice = sourceSliceIterator.getArrayNext();
sourceSlice.getArrayMath().eltDivideWithEqualSize(newArray, sourceSlice);
}
} catch (InvalidRangeException e) {
throw new ShapeNotMatchException("shape is invalid");
}
}
getArray().setDirty(true);
return this;
}
/**
* Element wise divide the value by value from a given array.
*
* @param array IArray object
* @return new array
* @throws ShapeNotMatchException mismatching shape
*/
public IArrayMath toEltDivide(IArray newArray) throws ShapeNotMatchException {
getArray().getArrayUtils().checkShape(newArray);
IArray result = getFactory().createArray(Double.TYPE, getArray().getShape());
if (getArray().getRank() == newArray.getRank()) {
eltDivideWithEqualSize(newArray, result);
} else {
ISliceIterator sourceSliceIterator = null;
ISliceIterator resultSliceIterator = null;
try {
sourceSliceIterator = getArray().getSliceIterator(newArray.getRank());
resultSliceIterator = result.getSliceIterator(newArray.getRank());
while (sourceSliceIterator.hasNext() && resultSliceIterator.hasNext()) {
IArray sourceSlice = sourceSliceIterator.getArrayNext();
IArray resultSlice = resultSliceIterator.getArrayNext();
sourceSlice.getArrayMath().eltDivideWithEqualSize(newArray, resultSlice);
}
} catch (InvalidRangeException e) {
throw new ShapeNotMatchException("shape is invalid");
}
}
return result.getArrayMath();
}
public Boolean process() throws Exception {
if (skip_norm) {
normalised_data = normalise_groupdata;
return false;
}
// Read in needed data and prepare output arrays
IArray dataArray = ((Plot) normalise_groupdata).findSignalArray();
IArray variance_array = ((Plot) normalise_groupdata).getVariance().getData();
/* If we have a 3D array, this means a series of monitor counts is available to
* allow normalisation at each scan step. If the array is 2D, then no such
* normalisation is necessary.
*/
double monmax = 1.0; // Value to which we normalise; usually maximum monitor counts
if (dataArray.getRank() > 2) {
int[] data_dims = dataArray.getShape();
int dlength = dataArray.getRank();
IArray monitor_array =
normalise_groupdata.getRootGroup().getDataItem("monitor_data").getData();
// We should have a monitor array rather than a single value
monmax = monitor_array.getArrayMath().getMaximum();
System.out.printf("Normalising to %f monitor counts\n", monmax);
IArray outarray = Factory.createArray(double.class, data_dims);
IArray out_variance = Factory.createArray(double.class, data_dims);
int[] origin_list = new int[dlength];
int[] range_list = new int[dlength];
int[] target_shape = new int[dlength]; // shape to make a 2D array from multi-dim array
for (int i = 0; i < dlength; i++) origin_list[i] = 0; // take in all elements
for (int i = 0; i < dlength - 2; i++) {
range_list[i] = data_dims[i]; // last two are rank reduced
target_shape[i] = 1;
}
range_list[dlength - 2] = 1;
range_list[dlength - 1] = 1;
target_shape[dlength - 2] = data_dims[dlength - 2];
target_shape[dlength - 1] = data_dims[dlength - 1];
// Create an iterator over the higher dimensions. We leave in the final two dimensions so
// that we can
// use the getCurrentCounter method to create an origin.
// Iterate over two-dimensional slices
// Array loop_array = dataArray.sectionNoReduce(origin_list, range_list, null);
ISliceIterator higher_dim_iter = dataArray.getSliceIterator(2);
IArrayIterator mon_iter = monitor_array.getIterator();
ISliceIterator high_dim_out_it = outarray.getSliceIterator(2);
ISliceIterator var_out_it = out_variance.getSliceIterator(2);
ISliceIterator invar_iter = variance_array.getSliceIterator(2);
// Now loop over higher dimensional frames
while (higher_dim_iter.hasNext()) {
double monval = mon_iter.getDoubleNext();
if (monval == 0) {
String errorstring = "Normalisation error: zero monitor counts found";
throw new Exception(errorstring);
}
double monerr = monmax * monmax / (monval * monval * monval);
monval = monmax / monval; // Actual value to multiply by
IArray[] out_with_var = {high_dim_out_it.getArrayNext(), var_out_it.getArrayNext()};
IArray[] in_with_var = {higher_dim_iter.getArrayNext(), invar_iter.getArrayNext()};
// First create a scalar for normalisation of each frame
double[] norm_with_err = {monval, monerr};
ArrayOperations.multiplyByScalar(in_with_var, norm_with_err, out_with_var);
}
// Now build the output databag
String resultName = "normalisation_result";
normalised_data = PlotFactory.createPlot(normalise_groupdata, resultName, dataDimensionType);
((NcGroup) normalised_data).addLog("Apply normalisation to get " + resultName);
PlotFactory.addDataToPlot(
normalised_data,
resultName,
outarray,
"Normalised data",
"Normalised counts",
out_variance);
// Copy axes across from previous data
List<Axis> data_axes = ((Plot) normalise_groupdata).getAxisList();
for (Axis oneaxis : data_axes) {
PlotFactory.addAxisToPlot(normalised_data, oneaxis, oneaxis.getDimensionName());
}
// We need the value to which everything has been normalised to be available to
// subsequent processing steps
IAttribute norm_val = Factory.createAttribute("normalised_to_val", monmax);
// normalised_data.buildResultGroup(signal, stthVector, channelVector, twoThetaVector);
normalised_data.addOneAttribute(norm_val);
} else {
normalised_data = normalise_groupdata;
}
return false;
}
