New algorithm to find multiple UB matrices in case of multiple domains or spurious peaks

[RichardWaiteSTFC]

Description of work

FindMultipleUMatrices will use the lattice parameters and spacegroup provided to optimise a number (NumberOfUBs) of UB matrices (B is hard-coded due to the lattice parameters provided) and return a group of peak workspaces one for each UB, containing the peaks that are indexed most accurately by that UB.

This algorithm is useful for finding a single UB in the presence of spurious peaks, or finding multiple UBs when there are multiple domains.

Fixes #35711

To test:

(1) Run the doc test code locally

from scipy.spatial.transform import Rotation as rot

ws = LoadEmptyInstrument(InstrumentName='SXD', OutputWorkspace='empty_SXD')
axis = ws.getAxis(0)
axis.setUnit("TOF")

# create a peak tables of orthorhombic domains with lattice parameters a=4, b=5, c=10
alatt = {'a': 4, 'b': 5, 'c': 9, 'alpha': 90, 'beta': 90, 'gamma': 90}
ubs = [np.diag([1/alatt['a'], 1/alatt['b'], 1/alatt['c']])]
ubs.append(rot.from_rotvec([0,0,90], degrees=True).as_matrix()  @ ubs[0])

peaks = CreatePeaksWorkspace(InstrumentWorkspace=ws, NumberOfPeaks=0, OutputWorkspace=f"peaks")
for iub, ub in enumerate(ubs):
    SetUB(peaks, UB=ub)
    for h in range(1, 3):
        for k in range(1, 3):
            for l in range(2,4):
                pk = peaks.createPeakHKL([h, k ,l])
                if pk.getDetectorID() > 0:
                    peaks.addPeak(pk)

peaks_out = FindMultipleUMatrices(PeaksWorkspace=peaks, OutputWorkspace='peaks_out', **alatt, 
                                  MinDSpacing=1.25, MaxDSpacing=3.5, Spacegroup='P m m m',
                                  NumberOfUBs=2)

for ipk, pks in enumerate(peaks_out):
    print("HKL along x-axis of QLab = ", np.round(pks.sample().getOrientedLattice().getvVector()))

It should print

HKL along x-axis of QLab =  [ 4. -0.  0.]
HKL along x-axis of QLab =  [-0. -5.  0.]

or equivalent

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