4.7.7 - Laue Deconvolution Predictor - JdlLaueDeconvPredictor

John W. Campbell Introduction

This class stores the multiples (+singles) data for a Laue multiple set of harmonics and then tests to see whether individual reflections can be potentially deconvoluted from these based on the data given and a given wavelength normalisation curve.

Class, constructors and methods:

Class Details
Accessible Fields
Constructor Class Details

Class name:
Class definition:
public class JdlLaueDeconvPredictor
none Accessible Fields

The following accessible fields have been defined:

Vector<Equation> equations
boolean predicted_data
int nh
int nk
int nl
int maxharm_found Constructor Introduction

A single constructor is available to set up the 'empty' object. This object may be re-used as often as required by invoking the clear() method between each set of harmonics being investigated.


Default Constructor
Clear object - clear
Add spot - addSpot
Predict deconvolution - predictDeconvolution Default Constructor

This constructs a JdlLaueDeconvPredictor object with an initial capacity for 20 spot measurements to be incremented in blocks of 20 spots as required.

Constructor Definition:
public JdlLaueDeconvPredictor()
Parameters List:
none Clear object - clear

This method clears the stored spots list in preparation for another deconvolution test.

Method Definition:
public void clear()
Parameters List:
none Add spot - addSpot

This method is used to add the spots data for the current set of harmonics whose deconvolution is to be investigated.

Method Definition:
public boolean addSpot(JdlPredictedLaueSpot spot)
Parameters List:
The spot data to be added.
Method Return:
true if spot added OK, false if harmonic does not belong to the set of harmonics currently being processed (i.e. not the same nodal indices). Predict deconvolution - predictDeconvolution

After the required spots have been added, this method may be called to test the potential deconvolution of individual reflections from the multiple (and possibly also single) spots. If successful, a vector of 'deconvoluted' reflections will be returned.

Method Definition:
public Vector predictDeconvolution(JdlChebyshevPolynomial curv, double scal_max, JdlError err)
Parameters List:
The wavelength normalisation curve to be used in the deconvolution test. A curve generated by the testLambdaCurve(..) method of the JdlTestLambdaCurve object may, for example, be used.
A maximum scale factor (derived from the wavelength normalisation curve) to be used e.g. 25.0.
Error return flag. If an error was found, err.err will be returned as true (for flag values up to 3), a message will be returned in err.msg1 and the flag err.flag will be set as follows:
= -1, No spots defined for deconvolution test
= 1, No suitable spots for deconvolution test
= 2, No valid deconvolution equations found
= 3, No singles or deconvoluted multiples
= 4, Insufficient equations to deconvolute multiples
= 5, Matrix cannot be inverted </br>
Method Return:
A vector of deconvoluted spots (JdlPredictedLaueSpot objects). (may be null if err.err was set to true - see above).

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