def export_roi_to_workspace_mdhisto(self,
slicepoint: Sequence[Optional[float]],
bin_params: Sequence[float],
limits: tuple, transpose: bool):
"""
Export 2D ROI to a workspace.
:param slicepoint: ND sequence of either None or float. A float defines the point
in that dimension for the slice.
:param bin_params: ND sequence containing the number of bins for each dimension. Can be None for HistoWorkspaces
:param limits: An optional ND sequence containing limits for plotting dimensions. If
not provided the full extent of each dimension is used
:param transpose: If true then the limits are transposed w.r.t to the data
"""
workspace = self._get_ws()
if transpose:
# swap back to model order
limits = limits[1], limits[0]
xindex, yindex = _display_indices(slicepoint)
dim_limits = _dimension_limits(workspace, slicepoint, limits)
# Construct paramters to integrate everything first and overrid per cut
params = {
f'P{n+1}Bin': [*dim_limits[n]]
for n in range(workspace.getNumDims())
}
xdim_min, xdim_max = dim_limits[xindex]
ydim_min, ydim_max = dim_limits[yindex]
params['OutputWorkspace'] = self._roi_name
params[f'P{xindex+1}Bin'] = [xdim_min, 0, xdim_max]
params[f'P{yindex+1}Bin'] = [ydim_min, 0, ydim_max]
IntegrateMDHistoWorkspace(InputWorkspace=workspace, **params)
if transpose:
_inplace_transposemd(self._roi_name, axes=[yindex, xindex])
return f'ROI created: {self._roi_name}'
def export_cuts_to_workspace_mdhisto(self,
slicepoint: Sequence[Optional[float]],
bin_params: Sequence[float],
limits: tuple, transpose: bool,
dimension_indices: Sequence[int],
cut: str):
"""
Export 1D cuts in the X/Y direction for the extent.
:param slicepoint: ND sequence of either None or float. A float defines the point
in that dimension for the slice.
:param bin_params: ND sequence containing the number of bins for each dimension. Can be None for HistoWorkspaces
:param limits: An optional ND sequence containing limits for plotting dimensions. If
not provided the full extent of each dimension is used
:param transpose: If true then the limits are transposed w.r.t to the data
:param cut: A string denoting which type to export. Options=c,x,y.
"""
workspace = self._get_ws()
if transpose:
# swap back to model order
limits = limits[1], limits[0]
dim_limits = _dimension_limits(workspace, slicepoint, limits)
# Construct paramters to integrate everything first and overrid per cut
params = {
f'P{n + 1}Bin': [*dim_limits[n]]
for n in range(workspace.getNumDims())
}
xindex, yindex = _display_indices(slicepoint, transpose)
xcut_name, ycut_name, help_msg = self._cut_names(cut)
xdim_min, xdim_max = dim_limits[xindex]
ydim_min, ydim_max = dim_limits[yindex]
if xcut_name:
params['OutputWorkspace'] = xcut_name
params[f'P{xindex + 1}Bin'] = [xdim_min, 0, xdim_max]
IntegrateMDHistoWorkspace(InputWorkspace=workspace, **params)
_keep_dimensions(xcut_name, xindex)
if ycut_name:
params['OutputWorkspace'] = ycut_name
params[f'P{xindex + 1}Bin'] = [xdim_min, xdim_max]
params[f'P{yindex + 1}Bin'] = [ydim_min, 0, ydim_max]
IntegrateMDHistoWorkspace(InputWorkspace=workspace, **params)
_keep_dimensions(ycut_name, yindex)
return help_msg
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])
convolved = convolve(signal, G3D)
convolved_fft = np.fft.fftshift(np.fft.fftn(np.fft.fftshift(convolved)))
convolved_out = (convolved_fft * np.conj(convolved_fft)).real
# Create output workspace
CreateMDHistoWorkspace(SignalInput=out,
ErrorInput=out**2,
Dimensionality=3,
Extents=extents,
NumberOfBins='{},{},{}'.format(*signal.shape),
Names='x,y,z',
Units='A,A,A',
OutputWorkspace='output')
integ = IntegrateMDHistoWorkspace(ws, P3Bin='-0.1,0.1')
signal = integ.getSignalArray().copy()
G1D = Gaussian1DKernel(2).array
G3D = G1D * G1D.reshape((-1, 1)) * G1D.reshape((-1, 1, 1))
convolved = convolve(signal, G3D)
signal[np.isnan(signal)] = 0
signal[np.isinf(signal)] = 0
axes = (0, 1)
fft = np.fft.fftshift(np.fft.fftn(np.fft.fftshift(signal, axes=axes),
axes=axes),
axes=axes)
fft = np.fft.fftshift(np.fft.fftn(np.fft.fftshift(convolved, axes=axes),
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])