def test_HFIRCalculateGoniometer_HB3A_phi(self):
omega = np.deg2rad(42)
chi = np.deg2rad(-3)
phi = np.deg2rad(23)
R1 = np.array([
[np.cos(omega), 0, -np.sin(omega)], # omega 0,1,0,-1
[0, 1, 0],
[np.sin(omega), 0, np.cos(omega)]
])
R2 = np.array([
[np.cos(chi), np.sin(chi), 0], # chi 0,0,1,-1
[-np.sin(chi), np.cos(chi), 0],
[0, 0, 1]
])
R3 = np.array([
[np.cos(phi), 0, -np.sin(phi)], # phi 0,1,0,-1
[0, 1, 0],
[np.sin(phi), 0, np.cos(phi)]
])
R = np.dot(np.dot(R1, R2), R3)
wl = 1.54
k = 2 * np.pi / wl
theta = np.deg2rad(47)
phi = np.deg2rad(13)
q_lab = np.array([
-np.sin(theta) * np.cos(phi), -np.sin(theta) * np.sin(phi),
1 - np.cos(theta)
]) * k
q_sample = np.dot(np.linalg.inv(R), q_lab)
peaks = CreatePeaksWorkspace(OutputType="LeanElasticPeak",
NumberOfPeaks=0)
AddSampleLog(peaks, "Wavelength", str(wl), "Number")
SetGoniometer(peaks, Axis0='42,0,1,0,-1',
Axis1='-3,0,0,1,-1') # don't set phi
p = peaks.createPeakQSample(q_sample)
peaks.addPeak(p)
HFIRCalculateGoniometer(peaks,
OverrideProperty=True,
InnerGoniometer=True)
g = Goniometer()
g.setR(peaks.getPeak(0).getGoniometerMatrix())
YZY = g.getEulerAngles('YZY')
self.assertAlmostEqual(YZY[0], -42, delta=1e-10) # omega
self.assertAlmostEqual(YZY[1], 3, delta=1e-10) # chi
self.assertAlmostEqual(YZY[2], -23, delta=1e-1) # phi
self.assertAlmostEqual(peaks.getPeak(0).getWavelength(),
1.54,
delta=1e-10)
def test_get_hkls(self):
ws = CreateSimulationWorkspace("IRIS", BinParams="1,5,10")
peaks = CreatePeaksWorkspace(ws, 2)
reference = np.array([
[1, 1, 2],
[2, 1, 4],
])
peak = peaks.getPeak(0)
peak.setHKL(1, 1, 2)
peak = peaks.getPeak(1)
peak.setHKL(2, 1, 4)
hkl = indexing.get_hkls(peaks)
npt.assert_array_equal(hkl, reference)
def test_get_hkls(self):
ws = CreateSimulationWorkspace("IRIS", BinParams="1,5,10")
peaks = CreatePeaksWorkspace(ws, 2)
reference = np.array([
[1, 1, 2],
[2, 1, 4],
])
peak = peaks.getPeak(0)
peak.setHKL(1, 1, 2)
peak = peaks.getPeak(1)
peak.setHKL(2, 1, 4)
hkl = indexing.get_hkls(peaks)
npt.assert_array_equal(hkl, reference)
def test_basic_access(self):
sampleWs = CreateSampleWorkspace()
ws = CreatePeaksWorkspace(InstrumentWorkspace=sampleWs,NumberOfPeaks=1)
peak = ws.getPeak(0)
peak_shape = peak.getPeakShape()
self.assertTrue(isinstance(peak_shape, PeakShape))
self.assertEquals(peak_shape.shapeName(), "none")
def test_basic_access(self):
sampleWs = CreateSampleWorkspace()
ws = CreatePeaksWorkspace(InstrumentWorkspace=sampleWs,NumberOfPeaks=1)
peak = ws.getPeak(0)
peak_shape = peak.getPeakShape()
self.assertTrue(isinstance(peak_shape, PeakShape))
self.assertEqual(peak_shape.shapeName(), "none")
def setUp(self):
# IPeak cannot currently be instatiated so this is a quick way
# getting a handle to a peak object
ws = CreateSimulationWorkspace("SXD", BinParams="1,1,10")
peaks = CreatePeaksWorkspace(ws, 1)
self._peak = peaks.getPeak(0)
# tolerance for differences in q vectors that a recomputed
# on every call.
self._tolerance = 1e-2
def setUp(self):
# IPeak cannot currently be instatiated so this is a quick way
# getting a handle to a peak object
ws = CreateSimulationWorkspace("SXD", BinParams="1,1,10")
peaks = CreatePeaksWorkspace(ws, 1)
self._peak = peaks.getPeak(0)
# tolerance for differences in q vectors that a recomputed
# on every call.
self._tolerance = 1e-2
def test_HFIRCalculateGoniometer_HB2C_omega(self):
omega = np.deg2rad(42)
R = np.array([[np.cos(omega), 0, np.sin(omega)], [0, 1, 0],
[-np.sin(omega), 0, np.cos(omega)]])
wl = 1.54
k = 2 * np.pi / wl
theta = np.deg2rad(47)
phi = np.deg2rad(13)
q_lab = np.array([
-np.sin(theta) * np.cos(phi), -np.sin(theta) * np.sin(phi),
1 - np.cos(theta)
]) * k
q_sample = np.dot(np.linalg.inv(R), q_lab)
peaks = CreatePeaksWorkspace(OutputType="LeanElasticPeak",
NumberOfPeaks=0)
p = peaks.createPeakQSample(q_sample)
peaks.addPeak(p)
HFIRCalculateGoniometer(peaks, wl)
g = Goniometer()
g.setR(peaks.getPeak(0).getGoniometerMatrix())
YZY = g.getEulerAngles('YZY')
self.assertAlmostEqual(YZY[0], 42, delta=1e-10) # omega
self.assertAlmostEqual(YZY[1], 0, delta=1e-10) # chi
self.assertAlmostEqual(YZY[2], 0, delta=1e-10) # phi
self.assertAlmostEqual(peaks.getPeak(0).getWavelength(),
1.54,
delta=1e-10)
q_lab = np.array([
-np.sin(theta) * np.cos(phi), -np.sin(theta) * np.sin(phi),
1 - np.cos(theta)
]) * k
q_sample = np.dot(np.linalg.inv(R), q_lab)
peaks = CreatePeaksWorkspace(OutputType="LeanElasticPeak", NumberOfPeaks=0)
p = peaks.createPeakQSample(q_sample)
peaks.addPeak(p)
HFIRCalculateGoniometer(peaks, wl, OverrideProperty=True, InnerGoniometer=True)
g = Goniometer()
g.setR(peaks.getPeak(0).getGoniometerMatrix())
print(g.getEulerAngles('YZY'))
assert np.isclose(g.getEulerAngles('YZY')[0], 42)
chi = np.deg2rad(-3)
phi = np.deg2rad(23)
R1 = np.array([
[np.cos(omega), 0, -np.sin(omega)], # omega 0,1,0,-1
[0, 1, 0],
[np.sin(omega), 0, np.cos(omega)]
])
R2 = np.array([
[np.cos(chi), np.sin(chi), 0], # chi 0,0,1,-1
[-np.sin(chi), np.cos(chi), 0],
[0, 0, 1]
def PyExec(self):
input_workspaces = self._expand_groups()
outWS = self.getPropertyValue("OutputWorkspace")
CreatePeaksWorkspace(OutputType='LeanElasticPeak',
InstrumentWorkspace=input_workspaces[0],
NumberOfPeaks=0,
OutputWorkspace=outWS,
EnableLogging=False)
method = self.getProperty("Method").value
n_bkgr_pts = self.getProperty("NumBackgroundPts").value
n_fwhm = self.getProperty("WidthScale").value
scale = self.getProperty("ScaleFactor").value
chisqmax = self.getProperty("ChiSqMax").value
signalNoiseMin = self.getProperty("SignalNoiseMin").value
ll = self.getProperty("LowerLeft").value
ur = self.getProperty("UpperRight").value
startX = self.getProperty('StartX').value
endX = self.getProperty('EndX').value
use_lorentz = self.getProperty("ApplyLorentz").value
optmize_q = self.getProperty("OptimizeQVector").value
output_fit = self.getProperty("OutputFitResults").value
if output_fit and method != "Counts":
fit_results = WorkspaceGroup()
AnalysisDataService.addOrReplace(outWS + "_fit_results",
fit_results)
for inWS in input_workspaces:
tmp_inWS = '__tmp_' + inWS
IntegrateMDHistoWorkspace(InputWorkspace=inWS,
P1Bin=f'{ll[1]},{ur[1]}',
P2Bin=f'{ll[0]},{ur[0]}',
OutputWorkspace=tmp_inWS,
EnableLogging=False)
ConvertMDHistoToMatrixWorkspace(tmp_inWS,
OutputWorkspace=tmp_inWS,
EnableLogging=False)
data = ConvertToPointData(tmp_inWS,
OutputWorkspace=tmp_inWS,
EnableLogging=False)
run = mtd[inWS].getExperimentInfo(0).run()
scan_log = 'omega' if np.isclose(run.getTimeAveragedStd('phi'),
0.0) else 'phi'
scan_axis = run[scan_log].value
scan_step = (scan_axis[-1] - scan_axis[0]) / (scan_axis.size - 1)
data.setX(0, scan_axis)
y = data.extractY().flatten()
x = data.extractX().flatten()
__tmp_pw = CreatePeaksWorkspace(OutputType='LeanElasticPeak',
InstrumentWorkspace=inWS,
NumberOfPeaks=0,
EnableLogging=False)
if method != "Counts":
# fit against gaussian with flat background for both the Fitted and CountsWithFitting methods
fit_result = self._fit_gaussian(inWS, data, x, y, startX, endX,
output_fit)
if fit_result and fit_result.OutputStatus == 'success' and fit_result.OutputChi2overDoF < chisqmax:
B, A, peak_centre, sigma, _ = fit_result.OutputParameters.toDict(
)['Value']
_, errA, _, errs, _ = fit_result.OutputParameters.toDict(
)['Error']
if method == "Fitted":
integrated_intensity = A * sigma * np.sqrt(2 * np.pi)
# Convert correlation back into covariance
cor_As = (
fit_result.OutputNormalisedCovarianceMatrix.cell(
1, 4) / 100 *
fit_result.OutputParameters.cell(1, 2) *
fit_result.OutputParameters.cell(3, 2))
# σ^2 = 2π (A^2 σ_s^2 + σ_A^2 s^2 + 2 A s σ_As)
integrated_intensity_error = np.sqrt(
2 * np.pi * (A**2 * errs**2 + sigma**2 * errA**2 +
2 * A * sigma * cor_As))
elif method == "CountsWithFitting":
y = y[slice(
np.searchsorted(
x, peak_centre - 2.3548 * sigma * n_fwhm / 2),
np.searchsorted(
x, peak_centre + 2.3548 * sigma * n_fwhm / 2))]
# subtract out the fitted flat background
integrated_intensity = (y.sum() -
B * y.size) * scan_step
integrated_intensity_error = np.sum(
np.sqrt(y)) * scan_step
# update the goniometer position based on the fitted peak center
if scan_log == 'omega':
SetGoniometer(Workspace=__tmp_pw,
Axis0=f'{peak_centre},0,1,0,-1',
Axis1='chi,0,0,1,-1',
Axis2='phi,0,1,0,-1',
EnableLogging=False)
else:
SetGoniometer(Workspace=__tmp_pw,
Axis0='omega,0,1,0,-1',
Axis1='chi,0,0,1,-1',
Axis2=f'{peak_centre},0,1,0,-1',
EnableLogging=False)
else:
self.log().warning(
"Failed to fit workspace {}: Output Status={}, ChiSq={}"
.format(inWS, fit_result.OutputStatus,
fit_result.OutputChi2overDoF))
self._delete_tmp_workspaces(str(__tmp_pw), tmp_inWS)
continue
else:
integrated_intensity, integrated_intensity_error = self._counts_integration(
data, n_bkgr_pts, scan_step)
# set the goniometer position to use the average of the scan
SetGoniometer(Workspace=__tmp_pw,
Axis0='omega,0,1,0,-1',
Axis1='chi,0,0,1,-1',
Axis2='phi,0,1,0,-1',
EnableLogging=False)
integrated_intensity *= scale
integrated_intensity_error *= scale
peak = __tmp_pw.createPeakHKL([
run['h'].getStatistics().median,
run['k'].getStatistics().median,
run['l'].getStatistics().median
])
peak.setWavelength(float(run['wavelength'].value))
peak.setIntensity(integrated_intensity)
peak.setSigmaIntensity(integrated_intensity_error)
if integrated_intensity / integrated_intensity_error > signalNoiseMin:
__tmp_pw.addPeak(peak)
# correct q-vector using CentroidPeaksMD
if optmize_q:
__tmp_q_ws = HB3AAdjustSampleNorm(InputWorkspaces=inWS,
NormaliseBy='None',
EnableLogging=False)
__tmp_pw = CentroidPeaksMD(__tmp_q_ws,
__tmp_pw,
EnableLogging=False)
DeleteWorkspace(__tmp_q_ws, EnableLogging=False)
if use_lorentz:
# ILL Neutron Data Booklet, Second Edition, Section 2.9, Part 4.1, Equation 7
peak = __tmp_pw.getPeak(0)
lorentz = abs(
np.sin(peak.getScattering() *
np.cos(peak.getAzimuthal())))
peak.setIntensity(peak.getIntensity() * lorentz)
peak.setSigmaIntensity(peak.getSigmaIntensity() * lorentz)
CombinePeaksWorkspaces(outWS,
__tmp_pw,
OutputWorkspace=outWS,
EnableLogging=False)
if output_fit and method != "Counts":
fit_results.addWorkspace(
RenameWorkspace(tmp_inWS + '_Workspace',
outWS + "_" + inWS + '_Workspace',
EnableLogging=False))
fit_results.addWorkspace(
RenameWorkspace(tmp_inWS + '_Parameters',
outWS + "_" + inWS + '_Parameters',
EnableLogging=False))
fit_results.addWorkspace(
RenameWorkspace(
tmp_inWS + '_NormalisedCovarianceMatrix',
outWS + "_" + inWS + '_NormalisedCovarianceMatrix',
EnableLogging=False))
fit_results.addWorkspace(
IntegrateMDHistoWorkspace(
InputWorkspace=inWS,
P1Bin=f'{ll[1]},0,{ur[1]}',
P2Bin=f'{ll[0]},0,{ur[0]}',
P3Bin='0,{}'.format(
mtd[inWS].getDimension(2).getNBins()),
OutputWorkspace=outWS + "_" + inWS + "_ROI",
EnableLogging=False))
else:
self.log().warning(
"Skipping peak from {} because Signal/Noise={:.3f} which is less than {}"
.format(inWS,
integrated_intensity / integrated_intensity_error,
signalNoiseMin))
self._delete_tmp_workspaces(str(__tmp_pw), tmp_inWS)
self.setProperty("OutputWorkspace", mtd[outWS])
def PyExec(self):
input_workspaces = self._expand_groups()
outWS = self.getPropertyValue("OutputWorkspace")
CreatePeaksWorkspace(OutputType='LeanElasticPeak',
InstrumentWorkspace=input_workspaces[0],
NumberOfPeaks=0,
OutputWorkspace=outWS,
EnableLogging=False)
scale = self.getProperty("ScaleFactor").value
chisqmax = self.getProperty("ChiSqMax").value
signalNoiseMin = self.getProperty("SignalNoiseMin").value
ll = self.getProperty("LowerLeft").value
ur = self.getProperty("UpperRight").value
startX = self.getProperty('StartX').value
endX = self.getProperty('EndX').value
use_lorentz = self.getProperty("ApplyLorentz").value
optmize_q = self.getProperty("OptimizeQVector").value
output_fit = self.getProperty("OutputFitResults").value
if output_fit:
fit_results = WorkspaceGroup()
AnalysisDataService.addOrReplace(outWS + "_fit_results",
fit_results)
for inWS in input_workspaces:
tmp_inWS = '__tmp_' + inWS
IntegrateMDHistoWorkspace(InputWorkspace=inWS,
P1Bin=f'{ll[1]},{ur[1]}',
P2Bin=f'{ll[0]},{ur[0]}',
OutputWorkspace=tmp_inWS,
EnableLogging=False)
ConvertMDHistoToMatrixWorkspace(tmp_inWS,
OutputWorkspace=tmp_inWS,
EnableLogging=False)
data = ConvertToPointData(tmp_inWS,
OutputWorkspace=tmp_inWS,
EnableLogging=False)
run = mtd[inWS].getExperimentInfo(0).run()
scan_log = 'omega' if np.isclose(run.getTimeAveragedStd('phi'),
0.0) else 'phi'
scan_axis = run[scan_log].value
data.setX(0, scan_axis)
y = data.extractY().flatten()
x = data.extractX().flatten()
function = f"name=FlatBackground, A0={np.nanmin(y)};" \
f"name=Gaussian, PeakCentre={x[np.nanargmax(y)]}, Height={np.nanmax(y)-np.nanmin(y)}, Sigma=0.25"
constraints = f"f0.A0 > 0, f1.Height > 0, {x.min()} < f1.PeakCentre < {x.max()}"
try:
fit_result = Fit(function,
data,
Output=str(data),
IgnoreInvalidData=True,
OutputParametersOnly=not output_fit,
Constraints=constraints,
StartX=startX,
EndX=endX,
EnableLogging=False)
except RuntimeError as e:
self.log().warning("Failed to fit workspace {}: {}".format(
inWS, e))
continue
if fit_result.OutputStatus == 'success' and fit_result.OutputChi2overDoF < chisqmax:
__tmp_pw = CreatePeaksWorkspace(OutputType='LeanElasticPeak',
InstrumentWorkspace=inWS,
NumberOfPeaks=0,
EnableLogging=False)
_, A, x, s, _ = fit_result.OutputParameters.toDict()['Value']
_, errA, _, errs, _ = fit_result.OutputParameters.toDict(
)['Error']
if scan_log == 'omega':
SetGoniometer(Workspace=__tmp_pw,
Axis0=f'{x},0,1,0,-1',
Axis1='chi,0,0,1,-1',
Axis2='phi,0,1,0,-1',
EnableLogging=False)
else:
SetGoniometer(Workspace=__tmp_pw,
Axis0='omega,0,1,0,-1',
Axis1='chi,0,0,1,-1',
Axis2=f'{x},0,1,0,-1',
EnableLogging=False)
peak = __tmp_pw.createPeakHKL([
run['h'].getStatistics().median,
run['k'].getStatistics().median,
run['l'].getStatistics().median
])
peak.setWavelength(float(run['wavelength'].value))
integrated_intensity = A * s * np.sqrt(2 * np.pi) * scale
peak.setIntensity(integrated_intensity)
# Convert correlation back into covariance
cor_As = (
fit_result.OutputNormalisedCovarianceMatrix.cell(1, 4) /
100 * fit_result.OutputParameters.cell(1, 2) *
fit_result.OutputParameters.cell(3, 2))
# σ^2 = 2π (A^2 σ_s^2 + σ_A^2 s^2 + 2 A s σ_As)
integrated_intensity_error = np.sqrt(
2 * np.pi * (A**2 * errs**2 + s**2 * errA**2 +
2 * A * s * cor_As)) * scale
peak.setSigmaIntensity(integrated_intensity_error)
if integrated_intensity / integrated_intensity_error > signalNoiseMin:
__tmp_pw.addPeak(peak)
# correct q-vector using CentroidPeaksMD
if optmize_q:
__tmp_q_ws = HB3AAdjustSampleNorm(InputWorkspaces=inWS,
NormaliseBy='None',
EnableLogging=False)
__tmp_pw = CentroidPeaksMD(__tmp_q_ws,
__tmp_pw,
EnableLogging=False)
DeleteWorkspace(__tmp_q_ws, EnableLogging=False)
if use_lorentz:
# ILL Neutron Data Booklet, Second Edition, Section 2.9, Part 4.1, Equation 7
peak = __tmp_pw.getPeak(0)
lorentz = abs(
np.sin(peak.getScattering() *
np.cos(peak.getAzimuthal())))
peak.setIntensity(peak.getIntensity() * lorentz)
peak.setSigmaIntensity(peak.getSigmaIntensity() *
lorentz)
CombinePeaksWorkspaces(outWS,
__tmp_pw,
OutputWorkspace=outWS,
EnableLogging=False)
DeleteWorkspace(__tmp_pw, EnableLogging=False)
if output_fit:
fit_results.addWorkspace(
RenameWorkspace(tmp_inWS + '_Workspace',
outWS + "_" + inWS + '_Workspace',
EnableLogging=False))
fit_results.addWorkspace(
RenameWorkspace(tmp_inWS + '_Parameters',
outWS + "_" + inWS + '_Parameters',
EnableLogging=False))
fit_results.addWorkspace(
RenameWorkspace(tmp_inWS +
'_NormalisedCovarianceMatrix',
outWS + "_" + inWS +
'_NormalisedCovarianceMatrix',
EnableLogging=False))
fit_results.addWorkspace(
IntegrateMDHistoWorkspace(
InputWorkspace=inWS,
P1Bin=f'{ll[1]},0,{ur[1]}',
P2Bin=f'{ll[0]},0,{ur[0]}',
P3Bin='0,{}'.format(
mtd[inWS].getDimension(2).getNBins()),
OutputWorkspace=outWS + "_" + inWS + "_ROI",
EnableLogging=False))
else:
self.log().warning(
"Skipping peak from {} because Signal/Noise={:.3f} which is less than {}"
.format(
inWS,
integrated_intensity / integrated_intensity_error,
signalNoiseMin))
else:
self.log().warning(
"Failed to fit workspace {}: Output Status={}, ChiSq={}".
format(inWS, fit_result.OutputStatus,
fit_result.OutputChi2overDoF))
for tmp_ws in (tmp_inWS, tmp_inWS + '_Workspace',
tmp_inWS + '_Parameters',
tmp_inWS + '_NormalisedCovarianceMatrix'):
if mtd.doesExist(tmp_ws):
DeleteWorkspace(tmp_ws, EnableLogging=False)
self.setProperty("OutputWorkspace", mtd[outWS])