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LeanElasticPeaks Workspace¶

The LeanElasticPeaksWorkspace is a special Workspace that holds a list of single crystal LeanElasticPeak objects. It is the equivalent to the PeaksWorkspace for Peak objects.

Creating a LeanElasticPeaksWorkspace¶

FindPeaksMD will find peaks in reciprocal space in a MDWorkspace.
PredictPeaks will predict peaks in reciprocal space for a given UB matrix.
CreatePeaksWorkspace will create an empty LeanElasticPeaksWorkspace that you can then edit. _e.g._ CreatePeaksWorkspace(NumberOfPeaks=0, OutputType="LeanElasticPeak", OutputWorkspace='peaks')

FindPeaksMD example¶

Create a MDEventWorkspace and add some fake peaks at Q-sample of (1,1,1), (3,3,3), and (5.5,3.14159,1) and run FindPeaksMD.

CreateMDWorkspace(Dimensions='3', Extents='0,10,0,10,0,10',
                  Names='Q_sample_x,Q_sample_y,Q_sample_z',
                  Units='rlu,rlu,rlu',
                  Frames='QSample,QSample,QSample',
                  OutputWorkspace='MDE')
FakeMDEventData('MDE', PeakParams='10000,1,1,1,0.2')
FakeMDEventData('MDE', PeakParams='100000,3,3,3,0.3')
FakeMDEventData('MDE', PeakParams='1000,5.5,3.14159,1,0.1')

peaks=FindPeaksMD('MDE',
                  DensityThresholdFactor=1,
                  PeakDistanceThreshold=1)

for n in range(peaks.getNumberPeaks()):
    p = peaks.getPeak(n)
    print(f"Peak {n} - Q-sample = {p.getQSampleFrame()} - bin count = {p.getBinCount()}")

Output:

Peak 0 - Q-sample = [5.50028,3.14201,0.999732] - bin count = 1000.0
Peak 1 - Q-sample = [3.08182,3.08072,3.08116] - bin count = 754.0
Peak 2 - Q-sample = [1.08179,0.918553,0.919687] - bin count = 257.0

Now convert that MDEventWorkspace to MDHistoWorkspace and run FindPeaksMD on that.

BinMD('MDE', OutputWorkspace='MDH',
      AlignedDim0='Q_sample_x,0,10,100',
      AlignedDim1='Q_sample_y,0,10,100',
      AlignedDim2='Q_sample_z,0,10,100')

peaks2=FindPeaksMD('MDH',
                   DensityThresholdFactor=1,
                   PeakDistanceThreshold=1)

for n in range(peaks2.getNumberPeaks()):
    p = peaks2.getPeak(n)
    print(f"Peak {n} - Q-sample = {p.getQSampleFrame()} - bin count = {p.getBinCount():.2f}")

Output:

Peak 0 - Q-sample = [3.15,2.85,3.15] - bin count = 1.43
Peak 1 - Q-sample = [0.95,0.95,1.05] - bin count = 0.39
Peak 2 - Q-sample = [5.45,3.15,0.95] - bin count = 0.18

PredictPeaks example¶

Create a workspace, set the UB and then predict all peaks in the d-spacing range.

ws=CreatePeaksWorkspace()
SetUB(ws,a=5,b=5,c=7,gamma=120)
peaks = PredictPeaks(ws, OutputType='LeanElasticPeak', CalculateWavelength=False, MinDSpacing=2.5, MaxDSpacing=3.5)
for n in range(peaks.getNumberPeaks()):
    p = peaks.getPeak(n)
    print(f"Peak {n:>2} - d-spacing = {p.getDSpacing():.2f} - HKL = {p.getHKL()}")

Output:

Peak  0 - d-spacing = 2.50 - HKL = [-2,1,0]
Peak  1 - d-spacing = 2.50 - HKL = [-1,-1,0]
Peak  2 - d-spacing = 2.72 - HKL = [-1,0,-2]
Peak  3 - d-spacing = 2.72 - HKL = [-1,0,2]
Peak  4 - d-spacing = 2.72 - HKL = [-1,1,-2]
Peak  5 - d-spacing = 2.72 - HKL = [-1,1,2]
Peak  6 - d-spacing = 2.50 - HKL = [-1,2,0]
Peak  7 - d-spacing = 2.72 - HKL = [0,-1,-2]
Peak  8 - d-spacing = 2.72 - HKL = [0,-1,2]
Peak  9 - d-spacing = 3.50 - HKL = [0,0,-2]
Peak 10 - d-spacing = 3.50 - HKL = [0,0,2]
Peak 11 - d-spacing = 2.72 - HKL = [0,1,-2]
Peak 12 - d-spacing = 2.72 - HKL = [0,1,2]
Peak 13 - d-spacing = 2.50 - HKL = [1,-2,0]
Peak 14 - d-spacing = 2.72 - HKL = [1,-1,-2]
Peak 15 - d-spacing = 2.72 - HKL = [1,-1,2]
Peak 16 - d-spacing = 2.72 - HKL = [1,0,-2]
Peak 17 - d-spacing = 2.72 - HKL = [1,0,2]
Peak 18 - d-spacing = 2.50 - HKL = [1,1,0]
Peak 19 - d-spacing = 2.50 - HKL = [2,-1,0]

Viewing a LeanElasticPeaksWorkspace¶

Double-click a LeanElasticPeaksWorkspace to see the full list of data of each Peak object.
The LeanElasticPeaksWorkspace can be overlay onto of data using the Mantid Workbench Sliceviewer

The LeanElasticPeak Object¶

Each peak object contains several pieces of information. Not all of them are necessary:

Q position (in q-sample frame)
H K L indices (optional)
Goniometer rotation matrix (for converting to Q in the lab frame)
Wavelength
Integrated intensity and error (optional)
An integration shape (see below)

The LeanElasticPeak Shape¶

This is the same as the Peak object, see PeaksWorkspace #The Peak Shape.

Using LeanElasticPeaksWorkspaces in Python¶

The LeanElasticPeaksWorkspace and LeanElasticPeak objects are exposed to python.

LeanElasticPeaksWorkspace Python Interface¶

See IPeaksWorkspace for complete API.

To get peaks from an existing workspace ‘name_of_peaks_workspace’

pws = mtd['name_of_peaks_workspace']
pws.getNumberPeaks()
p = pws.getPeak(12)
pws.removePeak(34)

The command p = pws.getPeak will give you a reference to the peak and not a copy of the peak, so any modification to p will change not just p but that peak in the name_of_peaks_workspace workspace.

To create an empty LeanElasticPeaksWorkspace then add peaks to it:

peaks = CreatePeaksWorkspace(NumberOfPeaks=0, OutputType="LeanElasticPeak")
p0 = peaks.createPeakQSample([1,1,1])
peaks.addPeak(p0)
SetUB(peaks, a=2, b=2, c=2)
p1 = peaks.createPeakHKL([1,2,3])
peaks.addPeak(p1)
for n in range(2):
    peak = peaks.getPeak(n)
    print('Peak {} hkl = {:.0f}{:.0f}{:.0f} q_sample = {}'.format(n, peak.getH(), peak.getK(), peak.getL(), peak.getQSampleFrame()))

Output:

Peak 0 hkl = 000 q_sample = [1,1,1]
Peak 1 hkl = 123 q_sample = [6.28319,9.42478,3.14159]

LeanElasticPeak Python Interface¶

See IPeak for complete API.

You can get a handle to an existing peak with:

p = peaks.getPeak(1)

Or you can create a new peak in this way:

qsample = V3D(1.23, 3.45, 2.22) # Q in the sample frame of the peak
p = peaks.createPeakQSample(qsample)
# The peak can later be added to the workspace
peaks.addPeak(p)

Once you have a handle on a peak “p” you have several methods to query/modify its values:

p.setHKL(-5, 4, 3)
hkl = p.getHKL()
print("hkl =", hkl)

q = p.getQSampleFrame()
print("q =", q)

p.setIntensity(1000.0)
p.setSigmaIntensity(31.6)
counts = p.getIntensity()
print("counts =", counts)

wl = p.getWavelength()
print("wl = {:.2f}".format(wl))
d = p.getDSpacing()
print("d = {:.2f}".format(d))
shape = p.getPeakShape()
print("shape =", shape.shapeName())

Output:

hkl = [-5,4,3]
q = [1.23,3.45,2.22]
counts = 1000.0
wl = 1.52
d = 1.47
shape = none

Category: Concepts