def output_data(self, x, y, e, data, NSpec, UnitX, norm, outputfn,
metadata):
"""Save reduced data to file"""
createWS_alg = self.createChildAlgorithm("CreateWorkspace",
enableLogging=False)
createWS_alg.setProperty("DataX", x)
createWS_alg.setProperty("DataY", y)
createWS_alg.setProperty("DataE", e)
createWS_alg.setProperty("NSpec", NSpec)
createWS_alg.setProperty("UnitX", UnitX)
createWS_alg.setProperty("YUnitLabel", "Counts")
createWS_alg.setProperty("WorkspaceTitle",
str(metadata['scan_title']) + "_norm_" + norm)
createWS_alg.execute()
outWS = createWS_alg.getProperty("OutputWorkspace").value
AnalysisDataService.addOrReplace(outputfn + "_norm_" + norm, outWS)
self.add_metadata(outWS, metadata, data)
# save reduced workspace to requested format
save_data = self.getProperty("SaveData").value
if save_data:
outputdir = self.getProperty("OutputDirectory").value
outputdir = outputdir if outputdir != "" else f"/HFIR/HB2A/IPTS-{metadata['proposal']}/shared"
_outputfunc = {
'XYE': SaveFocusedXYE,
'GSAS': SaveGSSCW
}[self.getProperty('OutputFormat').value]
_outputext = {
"XYE": 'dat',
"GSAS": 'gss',
}[self.getProperty('OutputFormat').value]
outputbase = os.path.join(outputdir, outputfn)
if norm == "mon":
out_f_name = outputbase
else:
out_f_name = outputbase + "_norm_" + norm
if self.getProperty('OutputFormat').value == "GSAS":
_outputfunc(
InputWorkspace=outWS,
OutputFilename=f"{out_f_name}.{_outputext}",
)
else:
_outputfunc(
InputWorkspace=outWS,
Filename=f"{out_f_name}.{_outputext}",
SplitFiles=False,
)
def PyExec(self):
scale = self.getProperty("Scale").value
filenames = self.getProperty("Filename").value
outputfn = self.getPropertyValue("OutputWorkspace")
if not filenames:
ipts = self.getProperty("IPTS").value
exp = self.getProperty("Exp").value
if self.getProperty("Exp").value == Property.EMPTY_INT:
exp = int([
e for e in os.listdir('/HFIR/HB2A/IPTS-{0}'.format(ipts))
if 'exp' in e
][0].replace('exp', ''))
filenames = [
'/HFIR/HB2A/IPTS-{0}/exp{1}/Datafiles/HB2A_exp{1:04}_scan{2:04}.dat'
.format(ipts, exp, scan)
for scan in self.getProperty("ScanNumbers").value
]
metadata = None
data = None
# Read in data array and append all files
for filename in filenames:
# Read in all lines once
with open(filename) as f:
lines = f.readlines()
if metadata is None:
# Read in metadata from first file only file
metadata = dict([
np.char.strip(
re.split('#(.*?)=(.*)', line, flags=re.U)[1:3])
for line in lines if re.match('^#.*=', line)
])
# Get indir and exp from first file
indir, data_filename = os.path.split(filename)
_, exp, _ = data_filename.replace(".dat", "").split('_')
# Find size of header, the size changes
header = np.argmax(
[bool(re.match('(?!^#)', line)) for line in lines]) - 1
if header < 0:
raise RuntimeError("{} has no data in it".format(filename))
names = lines[header].split()[1:]
try:
d = np.loadtxt(lines[header:],
ndmin=1,
dtype={
'names': names,
'formats': [float] * len(names)
})
except (ValueError, IndexError):
raise RuntimeError(
"Could not read {}, file likely malformed".format(
filename))
# Accumulate data
data = d if data is None else np.append(data, d)
# Get any masked detectors
detector_mask = self.get_detector_mask(exp, indir)
counts = np.array([data['anode{}'.format(n)]
for n in range(1, 45)])[detector_mask]
twotheta = data['2theta']
monitor = data['monitor']
# Remove points with zero monitor count
monitor_mask = np.nonzero(monitor)[0]
if len(monitor_mask) == 0:
raise RuntimeError(
"{} has all zero monitor counts".format(filename))
monitor = monitor[monitor_mask]
counts = counts[:, monitor_mask]
twotheta = twotheta[monitor_mask]
# Get either vcorr file or vanadium data
vanadium_count, vanadium_monitor, vcorr = self.get_vanadium(
detector_mask, data['m1'][0], data['colltrans'][0], exp, indir,
metadata)
def_x = self.getProperty("DefX").value
if not def_x:
def_x = metadata['def_x']
if def_x not in data.dtype.names:
logger.warning(
"Could not find {} property in datafile, using 2theta instead".
format(def_x))
def_x = '2theta'
if def_x == '2theta':
x = twotheta + self._gaps[:, np.newaxis][detector_mask]
UnitX = 'Degrees'
else:
x = np.tile(data[def_x], (44, 1))[detector_mask][:, monitor_mask]
UnitX = def_x
if self.getProperty("IndividualDetectors").value:
# Separate spectrum per anode
y, e = self.process(counts, scale, monitor, vanadium_count,
vanadium_monitor, vcorr)
NSpec = len(x)
else:
if self.getProperty("BinData").value:
# Data binned with bin
x, y, e = self.process_binned(counts, x.ravel(), scale,
monitor, vanadium_count,
vanadium_monitor, vcorr)
else:
y, e = self.process(counts, scale, monitor, vanadium_count,
vanadium_monitor, vcorr)
NSpec = 1
createWS_alg = self.createChildAlgorithm("CreateWorkspace",
enableLogging=False)
createWS_alg.setProperty("DataX", x)
createWS_alg.setProperty("DataY", y)
createWS_alg.setProperty("DataE", e)
createWS_alg.setProperty("NSpec", NSpec)
createWS_alg.setProperty("UnitX", UnitX)
createWS_alg.setProperty("YUnitLabel", "Counts")
createWS_alg.setProperty("WorkspaceTitle", str(metadata['scan_title']))
createWS_alg.execute()
outWS = createWS_alg.getProperty("OutputWorkspace").value
AnalysisDataService.addOrReplace(outputfn, outWS)
self.setProperty("OutputWorkspace", outWS)
self.add_metadata(outWS, metadata, data)
# save reduced workspace to requested format
save_data = self.getProperty("SaveData").value
if save_data:
outputdir = self.getProperty("OutputDirectory").value
outputdir = outputdir if outputdir != "" else f"/HFIR/HB2A/IPTS-{metadata['proposal']}/shared"
_outputfunc = {
'XYE': SaveFocusedXYE,
'GSAS': SaveGSSCW
}[self.getProperty('OutputFormat').value]
_outputext = {
"XYE": 'dat',
"GSAS": 'gss',
}[self.getProperty('OutputFormat').value]
outputbase = os.path.join(outputdir, outputfn)
if self.getProperty('OutputFormat').value == "GSAS":
_outputfunc(
InputWorkspace=outWS,
OutputFilename=f"{outputbase}.{_outputext}",
)
else:
_outputfunc(
InputWorkspace=outWS,
Filename=f"{outputbase}.{_outputext}",
SplitFiles=False,
)
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])
