def load_file_and_apply(self, filename, ws_name, offset):
Load(Filename=filename,
OutputWorkspace=ws_name,
FilterByTofMin=self.getProperty("FilterByTofMin").value,
FilterByTofMax=self.getProperty("FilterByTofMax").value)
if self._load_inst:
LoadInstrument(Workspace=ws_name,
Filename=self.getProperty("LoadInstrument").value,
RewriteSpectraMap=False)
if self._apply_cal:
ApplyCalibration(
Workspace=ws_name,
CalibrationTable=self.getProperty("ApplyCalibration").value)
if self._detcal:
LoadIsawDetCal(InputWorkspace=ws_name,
Filename=self.getProperty("DetCal").value)
if self._copy_params:
CopyInstrumentParameters(OutputWorkspace=ws_name,
InputWorkspace=self.getProperty(
"CopyInstrumentParameters").value)
MaskDetectors(Workspace=ws_name, MaskedWorkspace='__sa')
if offset != 0:
if self.getProperty('SetGoniometer').value:
SetGoniometer(
Workspace=ws_name,
Goniometers=self.getProperty('Goniometers').value,
Axis0='{},0,1,0,1'.format(offset),
Axis1=self.getProperty('Axis0').value,
Axis2=self.getProperty('Axis1').value,
Axis3=self.getProperty('Axis2').value)
else:
SetGoniometer(Workspace=ws_name,
Axis0='{},0,1,0,1'.format(offset),
Axis1='omega,0,1,0,1',
Axis2='chi,0,0,1,1',
Axis3='phi,0,1,0,1')
else:
if self.getProperty('SetGoniometer').value:
SetGoniometer(
Workspace=ws_name,
Goniometers=self.getProperty('Goniometers').value,
Axis0=self.getProperty('Axis0').value,
Axis1=self.getProperty('Axis1').value,
Axis2=self.getProperty('Axis2').value)
ConvertUnits(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
Target='Momentum')
CropWorkspaceForMDNorm(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
XMin=self.XMin,
XMax=self.XMax)
def test_handles_inaccurate_goniometer(self):
peaks1 = CreatePeaksWorkspace(InstrumentWorkspace=self.ws,
NumberOfPeaks=0,
OutputWorkspace="SXD_peaks3")
peaks2 = CloneWorkspace(InputWorkspace=peaks1,
OutputWorkspace="SXD_peaks4")
# set different gonio on each run
rot = 5
SetGoniometer(Workspace=peaks1, Axis0=f'{-rot},0,1,0,1')
SetGoniometer(Workspace=peaks2, Axis0=f'{rot},0,1,0,1')
# Add peaks at QLab corresponding to slightly different gonio rotations
UB = np.diag([0.25, 0.25, 0.1]) # alatt = [4,4,10]
for h in range(0, 3):
for k in range(0, 3):
hkl = np.array([h, k, 4])
qlab = 2 * np.pi * np.matmul(
np.matmul(getR(-(rot + 1), [0, 1, 0]), UB), hkl)
pk = peaks1.createPeak(qlab)
peaks1.addPeak(pk)
qlab = 2 * np.pi * np.matmul(
np.matmul(getR(rot + 1, [0, 1, 0]), UB), hkl)
pk = peaks2.createPeak(qlab)
peaks2.addPeak(pk)
FindGlobalBMatrix(PeakWorkspaces=[peaks1, peaks2],
a=4.15,
b=3.95,
c=10,
alpha=88,
beta=88,
gamma=89,
Tolerance=0.15)
# check lattice - shouldn't be effected by error in goniometer
self.assert_lattice([peaks1, peaks2],
4.0,
4.0,
10.0,
90.0,
90.0,
90.0,
delta_latt=2e-2,
delta_angle=2.5e-1)
self.assert_matrix([peaks1],
getBMatrix(peaks2),
getBMatrix,
delta=1e-10) # should have same B matrix
self.assert_matrix([peaks1, peaks2], np.eye(3), getUMatrix, delta=5e-2)
def prepare_background(input_md, reference_sample_mde, background_md: str):
"""Prepare background MDEventWorkspace from reduced sample Matrix
This is the previous solution to merge all the ExperimentInfo
"""
dgs_data = CloneWorkspace(input_md)
data_MDE = mtd[reference_sample_mde]
if mtd.doesExist('background_MDE'):
DeleteWorkspace('background_MDE')
for i in range(data_MDE.getNumExperimentInfo()):
phi, chi, omega = data_MDE.getExperimentInfo(
i).run().getGoniometer().getEulerAngles('YZY')
AddSampleLogMultiple(Workspace=dgs_data,
LogNames='phi, chi, omega',
LogValues='{},{},{}'.format(phi, chi, omega))
SetGoniometer(Workspace=dgs_data, Goniometers='Universal')
ConvertToMD(InputWorkspace=dgs_data,
QDimensions='Q3D',
dEAnalysisMode='Direct',
Q3DFrames="Q_sample",
MinValues='-11,-11,-11,-25',
MaxValues='11,11,11,49',
PreprocDetectorsWS='-',
OverwriteExisting=False,
OutputWorkspace=background_md)
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_qtohkl_corelli(self):
Load(Filename='CORELLI_29782.nxs', OutputWorkspace='data')
SetGoniometer(Workspace='data', Axis0='BL9:Mot:Sample:Axis1,0,1,0,1')
LoadIsawUB(InputWorkspace='data',
Filename='SingleCrystalDiffuseReduction_UB.mat')
ConvertToMD(InputWorkspace='data',
QDimensions='Q3D',
dEAnalysisMode='Elastic',
Q3DFrames='HKL',
QConversionScales='HKL',
OutputWorkspace='HKL',
Uproj='1,1,0',
Vproj='1,-1,0',
Wproj='0,0,1',
MinValues='-20,-20,-20',
MaxValues='20,20,20')
hkl_binned = BinMD('HKL',
AlignedDim0='[H,H,0],-10.05,10.05,201',
AlignedDim1='[H,-H,0],-10.05,10.05,201',
AlignedDim2='[0,0,L],-1.05,1.05,21')
ConvertToMD(InputWorkspace='data',
QDimensions='Q3D',
dEAnalysisMode='Elastic',
Q3DFrames='Q_sample',
OutputWorkspace='q_sample',
MinValues='-20,-20,-20',
MaxValues='20,20,20')
hkl = ConvertQtoHKLMDHisto(
InputWorkspace=mtd["q_sample"],
Uproj="1,1,0",
Vproj="1,-1,0",
Wproj="0,0,1",
Extents="-10.05,10.05,-10.05,10.05,-1.05,1.05",
Bins="201,201,21")
for i in range(hkl.getNumDims()):
self.assertEqual(
hkl_binned.getDimension(i).name,
hkl.getDimension(i).name)
orig_sig = hkl_binned.getSignalArray()
new_sig = hkl.getSignalArray()
np.testing.assert_allclose(np.asarray(new_sig),
np.asarray(orig_sig),
rtol=1.0e-5,
atol=3)
def loadIntegrateData(filename, OutputWorkspace='__ws', wavelength=1.488):
LoadEventNexus(Filename=filename,
OutputWorkspace=OutputWorkspace,
LoadMonitors=True)
Integration(InputWorkspace=OutputWorkspace,
OutputWorkspace=OutputWorkspace)
MaskDetectors(OutputWorkspace, DetectorList=range(16384))
mtd[OutputWorkspace].getAxis(0).setUnit("Wavelength")
w = np.array([wavelength - 0.001, wavelength + 0.001])
for idx in range(mtd[OutputWorkspace].getNumberHistograms()):
mtd[OutputWorkspace].setX(idx, w)
SetGoniometer(OutputWorkspace, Axis0="HB2C:Mot:s1,0,1,0,1")
AddSampleLog(OutputWorkspace,
LogName="gd_prtn_chrg",
LogType='Number',
NumberType='Double',
LogText=str(mtd[OutputWorkspace +
'_monitors'].getNumberEvents()))
return OutputWorkspace
def PyExec(self):
filename = self.getProperty("Filename").value
wavelength = self.getProperty("wavelength").value
outWS = self.getPropertyValue("OutputWorkspace")
LoadEventNexus(Filename=filename,
OutputWorkspace=outWS,
LoadMonitors=True)
Integration(InputWorkspace=outWS, OutputWorkspace=outWS)
if self.getProperty("ApplyMask").value:
MaskBTP(outWS, Bank='8', Tube='449-480')
MaskBTP(outWS, Pixel='1,2,511,512')
mtd[outWS].getAxis(0).setUnit("Wavelength")
w = [wavelength - 0.001, wavelength + 0.001]
for idx in range(mtd[outWS].getNumberHistograms()):
mtd[outWS].setX(idx, w)
SetGoniometer(outWS, Axis0="HB2C:Mot:s1,0,1,0,1")
AddSampleLog(outWS,
LogName="gd_prtn_chrg",
LogType='Number',
NumberType='Double',
LogText=str(mtd[outWS + '_monitors'].getNumberEvents()))
DeleteWorkspace(outWS + '_monitors')
AddSampleLog(outWS,
LogName="Wavelength",
LogType='Number',
NumberType='Double',
LogText=str(wavelength))
AddSampleLog(outWS,
LogName="Ei",
LogType='Number',
NumberType='Double',
LogText=str(
UnitConversion.run('Wavelength', 'Energy', wavelength,
0, 0, 0, Elastic, 0)))
self.setProperty('OutputWorkspace', outWS)
def PyExec(self):
fn = self.getPropertyValue("Filename")
wsn = self.getPropertyValue("OutputWorkspace")
monitor_workspace_name = self.getPropertyValue(
"OutputMonitorWorkspace")
if monitor_workspace_name == "":
self.setPropertyValue("OutputMonitorWorkspace", wsn + '_Monitors')
# print (fn, wsn)
self.override_angle = self.getPropertyValue("AngleOverride")
self.fxml = self.getPropertyValue("InstrumentXML")
# load data
parms_dict, det_udet, det_count, det_tbc, data = self.read_file(fn)
nrows = int(parms_dict['NDET'])
# nbins=int(parms_dict['NTC'])
xdata = np.array(det_tbc)
xdata_mon = np.linspace(xdata[0], xdata[-1], len(xdata))
ydata = data.astype(float)
ydata = ydata.reshape(nrows, -1)
edata = np.sqrt(ydata)
# CreateWorkspace(OutputWorkspace=wsn,DataX=xdata,DataY=ydata,DataE=edata,
# NSpec=nrows,UnitX='TOF',WorkspaceTitle='Data',YUnitLabel='Counts')
nr, nc = ydata.shape
ws = WorkspaceFactory.create("Workspace2D",
NVectors=nr,
XLength=nc + 1,
YLength=nc)
for i in range(nrows):
ws.setX(i, xdata)
ws.setY(i, ydata[i])
ws.setE(i, edata[i])
ws.getAxis(0).setUnit('tof')
AnalysisDataService.addOrReplace(wsn, ws)
# self.setProperty("OutputWorkspace", wsn)
# print ("ws:", wsn)
# ws=mtd[wsn]
# fix the x values for the monitor
for i in range(nrows - 2, nrows):
ws.setX(i, xdata_mon)
self.log().information("set detector IDs")
# set detetector IDs
for i in range(nrows):
ws.getSpectrum(i).setDetectorID(det_udet[i])
# Sample_logs the header values are written into the sample logs
log_names = [
str(sl.encode('ascii', 'ignore').decode())
for sl in parms_dict.keys()
]
log_values = [
str(sl.encode('ascii', 'ignore').decode()) if isinstance(
sl, UnicodeType) else str(sl) for sl in parms_dict.values()
]
for i in range(len(log_values)):
if ('nan' in log_values[i]) or ('NaN' in log_values[i]):
log_values[i] = '-1.0'
AddSampleLogMultiple(Workspace=wsn,
LogNames=log_names,
LogValues=log_values)
SetGoniometer(Workspace=wsn, Goniometers='Universal')
if (self.fxml == ""):
LoadInstrument(Workspace=wsn,
InstrumentName="Exed",
RewriteSpectraMap=True)
else:
LoadInstrument(Workspace=wsn,
Filename=self.fxml,
RewriteSpectraMap=True)
try:
RotateInstrumentComponent(
Workspace=wsn,
ComponentName='Tank',
Y=1,
Angle=-float(parms_dict['phi'].encode('ascii', 'ignore')),
RelativeRotation=False)
except:
self.log().warning(
"The instrument does not contain a 'Tank' component. "
"This means that you are using a custom XML instrument definition. "
"OMEGA_MAG will be ignored.")
self.log().warning(
"Please make sure that the detector positions in the instrument definition are correct."
)
# Separate monitors into seperate workspace
__temp_monitors = ExtractSpectra(
InputWorkspace=wsn,
WorkspaceIndexList=','.join(
[str(s) for s in range(nrows - 2, nrows)]),
OutputWorkspace=self.getPropertyValue("OutputMonitorWorkspace"))
# ExtractSpectra(InputWorkspace = wsn, WorkspaceIndexList = ','.join([str(s) for s in range(nrows-2, nrows)]),
# OutputWorkspace = wsn + '_Monitors')
MaskDetectors(Workspace=wsn,
WorkspaceIndexList=','.join(
[str(s) for s in range(nrows - 2, nrows)]))
RemoveMaskedSpectra(InputWorkspace=wsn, OutputWorkspace=wsn)
self.setProperty("OutputWorkspace", wsn)
self.setProperty("OutputMonitorWorkspace", __temp_monitors)
def PyExec(self):
_load_inst = bool(self.getProperty("LoadInstrument").value)
_detcal = bool(self.getProperty("DetCal").value)
_masking = bool(self.getProperty("MaskFile").value)
_outWS_name = self.getPropertyValue("OutputWorkspace")
_UB = bool(self.getProperty("UBMatrix").value)
MinValues = self.getProperty("MinValues").value
MaxValues = self.getProperty("MaxValues").value
if self.getProperty("OverwriteExisting").value:
if mtd.doesExist(_outWS_name):
DeleteWorkspace(_outWS_name)
progress = Progress(self, 0.0, 1.0,
len(self.getProperty("Filename").value))
for run in self.getProperty("Filename").value:
logger.notice("Working on " + run)
Load(Filename=run,
OutputWorkspace='__run',
FilterByTofMin=self.getProperty("FilterByTofMin").value,
FilterByTofMax=self.getProperty("FilterByTofMax").value,
FilterByTimeStop=self.getProperty("FilterByTimeStop").value)
if _load_inst:
LoadInstrument(
Workspace='__run',
Filename=self.getProperty("LoadInstrument").value,
RewriteSpectraMap=False)
if _detcal:
LoadIsawDetCal(InputWorkspace='__run',
Filename=self.getProperty("DetCal").value)
if _masking:
if not mtd.doesExist('__mask'):
LoadMask(Instrument=mtd['__run'].getInstrument().getName(),
InputFile=self.getProperty("MaskFile").value,
OutputWorkspace='__mask')
MaskDetectors(Workspace='__run', MaskedWorkspace='__mask')
if self.getProperty('SetGoniometer').value:
SetGoniometer(
Workspace='__run',
Goniometers=self.getProperty('Goniometers').value,
Axis0=self.getProperty('Axis0').value,
Axis1=self.getProperty('Axis1').value,
Axis2=self.getProperty('Axis2').value)
if _UB:
LoadIsawUB(InputWorkspace='__run',
Filename=self.getProperty("UBMatrix").value)
if len(MinValues) == 0 or len(MaxValues) == 0:
MinValues, MaxValues = ConvertToMDMinMaxGlobal(
'__run',
dEAnalysisMode='Elastic',
Q3DFrames='HKL',
QDimensions='Q3D')
ConvertToMD(
InputWorkspace='__run',
OutputWorkspace=_outWS_name,
QDimensions='Q3D',
dEAnalysisMode='Elastic',
Q3DFrames='HKL',
QConversionScales='HKL',
Uproj=self.getProperty('Uproj').value,
Vproj=self.getProperty('Vproj').value,
Wproj=self.getProperty('Wproj').value,
MinValues=MinValues,
MaxValues=MaxValues,
SplitInto=self.getProperty('SplitInto').value,
SplitThreshold=self.getProperty('SplitThreshold').value,
MaxRecursionDepth=self.getProperty(
'MaxRecursionDepth').value,
OverwriteExisting=False)
else:
if len(MinValues) == 0 or len(MaxValues) == 0:
MinValues, MaxValues = ConvertToMDMinMaxGlobal(
'__run',
dEAnalysisMode='Elastic',
Q3DFrames='Q',
QDimensions='Q3D')
ConvertToMD(
InputWorkspace='__run',
OutputWorkspace=_outWS_name,
QDimensions='Q3D',
dEAnalysisMode='Elastic',
Q3DFrames='Q_sample',
Uproj=self.getProperty('Uproj').value,
Vproj=self.getProperty('Vproj').value,
Wproj=self.getProperty('Wproj').value,
MinValues=MinValues,
MaxValues=MaxValues,
SplitInto=self.getProperty('SplitInto').value,
SplitThreshold=self.getProperty('SplitThreshold').value,
MaxRecursionDepth=self.getProperty(
'MaxRecursionDepth').value,
OverwriteExisting=False)
DeleteWorkspace('__run')
progress.report()
if mtd.doesExist('__mask'):
DeleteWorkspace('__mask')
self.setProperty("OutputWorkspace", mtd[_outWS_name])
def PyExec(self):
load_van = not self.getProperty("VanadiumFile").isDefault
load_files = not self.getProperty("Filename").isDefault
output = self.getProperty("OutputType").value
if load_files:
datafiles = self.getProperty("Filename").value
else:
datafiles = list(
map(str.strip,
self.getProperty("InputWorkspaces").value.split(",")))
prog = Progress(self, 0.0, 1.0, len(datafiles) + 1)
vanadiumfile = self.getProperty("VanadiumFile").value
vanws = self.getProperty("VanadiumWorkspace").value
height = self.getProperty("DetectorHeightOffset").value
distance = self.getProperty("DetectorDistanceOffset").value
wslist = []
out_ws = self.getPropertyValue("OutputWorkspace")
out_ws_name = out_ws
if load_van:
vanws = LoadMD(vanadiumfile, StoreInADS=False)
has_multiple = len(datafiles) > 1
for in_file in datafiles:
if load_files:
scan = LoadMD(in_file,
LoadHistory=False,
OutputWorkspace="__scan")
else:
scan = mtd[in_file]
# Make sure the workspace has experiment info, otherwise SetGoniometer will add some, causing issues.
if scan.getNumExperimentInfo() == 0:
raise RuntimeError(
"No experiment info was found in '{}'".format(in_file))
prog.report()
self.log().information("Processing '{}'".format(in_file))
SetGoniometer(Workspace=scan,
Axis0='omega,0,1,0,-1',
Axis1='chi,0,0,1,-1',
Axis2='phi,0,1,0,-1',
Average=False)
# If processing multiple files, append the base name to the given output name
if has_multiple:
if load_files:
out_ws_name = out_ws + "_" + os.path.basename(
in_file).strip(',.nxs')
else:
out_ws_name = out_ws + "_" + in_file
wslist.append(out_ws_name)
exp_info = scan.getExperimentInfo(0)
self.__move_components(exp_info, height, distance)
# Get the wavelength from experiment info if it exists, or fallback on property value
wavelength = self.__get_wavelength(exp_info)
# set the run number to be the same as scan number, this will be used for peaks
if not exp_info.run().hasProperty('run_number') and exp_info.run(
).hasProperty('scan'):
try:
exp_info.mutableRun().addProperty(
'run_number',
int(exp_info.run().getProperty('scan').value), True)
except ValueError:
# scan must be a int
pass
# Use ConvertHFIRSCDtoQ (and normalize van), or use ConvertWANDSCtoQ which handles normalization itself
if output == "Q-sample events":
norm_data = self.__normalization(scan, vanws, load_files)
minvals = self.getProperty("MinValues").value
maxvals = self.getProperty("MaxValues").value
merge = self.getProperty("MergeInputs").value
ConvertHFIRSCDtoMDE(InputWorkspace=norm_data,
Wavelength=wavelength,
MinValues=minvals,
MaxValues=maxvals,
OutputWorkspace=out_ws_name)
DeleteWorkspace(norm_data)
elif output == 'Q-sample histogram':
bin0 = self.getProperty("BinningDim0").value
bin1 = self.getProperty("BinningDim1").value
bin2 = self.getProperty("BinningDim2").value
# Convert to Q space and normalize with from the vanadium
ConvertWANDSCDtoQ(
InputWorkspace=scan,
NormalisationWorkspace=vanws,
Frame='Q_sample',
Wavelength=wavelength,
NormaliseBy=self.getProperty("NormaliseBy").value,
BinningDim0=bin0,
BinningDim1=bin1,
BinningDim2=bin2,
OutputWorkspace=out_ws_name)
if load_files:
DeleteWorkspace(scan)
else:
norm_data = self.__normalization(scan, vanws, load_files)
RenameWorkspace(norm_data, OutputWorkspace=out_ws_name)
if has_multiple:
out_ws_name = out_ws
if output == "Q-sample events" and merge:
MergeMD(InputWorkspaces=wslist, OutputWorkspace=out_ws_name)
DeleteWorkspaces(wslist)
else:
GroupWorkspaces(InputWorkspaces=wslist,
OutputWorkspace=out_ws_name)
# Don't delete workspaces if they were passed in
if load_van:
DeleteWorkspace(vanws)
self.setProperty("OutputWorkspace", out_ws_name)
def _determine_single_crystal_diffraction(self):
"""
All work related to the determination of the diffraction pattern
"""
a, b, c = self.getProperty('LatticeSizes').value
alpha, beta, gamma = self.getProperty('LatticeAngles').value
u = self.getProperty('VectorU').value
v = self.getProperty('VectorV').value
uproj = self.getProperty('Uproj').value
vproj = self.getProperty('Vproj').value
wproj = self.getProperty('Wproj').value
n_bins = self.getProperty('NBins').value
self._n_bins = (n_bins, n_bins, 1)
axis0 = '{},0,1,0,1'.format(self.getProperty('PsiAngleLog').value)
axis1 = '{},0,1,0,1'.format(self.getProperty('PsiOffset').value)
# Options for SetUB independent of run
ub_args = dict(a=a,
b=b,
c=c,
alpha=alpha,
beta=beta,
gamma=gamma,
u=u,
v=v)
min_values = None
# Options for algorithm ConvertToMD independent of run
convert_to_md_kwargs = dict(QDimensions='Q3D',
dEAnalysisMode='Elastic',
Q3DFrames='HKL',
QConversionScales='HKL',
Uproj=uproj,
Vproj=vproj,
Wproj=wproj)
md_norm_scd_kwargs = None # Options for algorithm MDNormSCD
# Find solid angle and flux
if self._vanadium_files:
kwargs = dict(Filename='+'.join(self._vanadium_files),
MaskFile=self.getProperty("MaskFile").value,
MomentumMin=self._momentum_range[0],
MomentumMax=self._momentum_range[1])
_t_solid_angle, _t_int_flux = \
MDNormSCDPreprocessIncoherent(**kwargs)
else:
_t_solid_angle = self.nominal_solid_angle('_t_solid_angle')
_t_int_flux = self.nominal_integrated_flux('_t_int_flux')
# Process a sample at a time
run_numbers = self._getRuns(self.getProperty("RunNumbers").value,
doIndiv=True)
run_numbers = list(itertools.chain.from_iterable(run_numbers))
diffraction_reporter = Progress(self,
start=0.0,
end=1.0,
nreports=len(run_numbers))
for i_run, run in enumerate(run_numbers):
_t_sample = self._mask_t0_crop(run, '_t_sample')
# Set Goniometer and UB matrix
SetGoniometer(_t_sample, Axis0=axis0, Axis1=axis1)
SetUB(_t_sample, **ub_args)
if self._bkg:
self._bkg.run().getGoniometer().\
setR(_t_sample.run().getGoniometer().getR())
SetUB(self._bkg, **ub_args)
# Determine limits for momentum transfer in HKL space. Needs to be
# done only once. We use the first run.
if min_values is None:
kwargs = dict(QDimensions='Q3D',
dEAnalysisMode='Elastic',
Q3DFrames='HKL')
min_values, max_values = ConvertToMDMinMaxGlobal(
_t_sample, **kwargs)
convert_to_md_kwargs.update({
'MinValues': min_values,
'MaxValues': max_values
})
# Convert to MD
_t_md = ConvertToMD(_t_sample,
OutputWorkspace='_t_md',
**convert_to_md_kwargs)
if self._bkg:
_t_bkg_md = ConvertToMD(self._bkg,
OutputWorkspace='_t_bkg_md',
**convert_to_md_kwargs)
# Determine aligned dimensions. Need to be done only once
if md_norm_scd_kwargs is None:
aligned = list()
for i_dim in range(3):
kwargs = {
'name': _t_md.getDimension(i_dim).name,
'min': min_values[i_dim],
'max': max_values[i_dim],
'n_bins': self._n_bins[i_dim]
}
aligned.append(
'{name},{min},{max},{n_bins}'.format(**kwargs))
md_norm_scd_kwargs = dict(AlignedDim0=aligned[0],
AlignedDim1=aligned[1],
AlignedDim2=aligned[2],
FluxWorkspace=_t_int_flux,
SolidAngleWorkspace=_t_solid_angle,
SkipSafetyCheck=True)
# Normalize sample by solid angle and integrated flux;
# Accumulate runs into the temporary workspaces
MDNormSCD(_t_md,
OutputWorkspace='_t_data',
OutputNormalizationWorkspace='_t_norm',
TemporaryDataWorkspace='_t_data'
if mtd.doesExist('_t_data') else None,
TemporaryNormalizationWorkspace='_t_norm'
if mtd.doesExist('_t_norm') else None,
**md_norm_scd_kwargs)
if self._bkg:
MDNormSCD(_t_bkg_md,
OutputWorkspace='_t_bkg_data',
OutputNormalizationWorkspace='_t_bkg_norm',
TemporaryDataWorkspace='_t_bkg_data'
if mtd.doesExist('_t_bkg_data') else None,
TemporaryNormalizationWorkspace='_t_bkg_norm'
if mtd.doesExist('_t_bkg_norm') else None,
**md_norm_scd_kwargs)
message = 'Processing sample {} of {}'.\
format(i_run+1, len(run_numbers))
diffraction_reporter.report(message)
self._temps.workspaces.append('PreprocessedDetectorsWS') # to remove
# Iteration over the sample runs is done.
# Division by vanadium, subtract background, and rename workspaces
name = self.getPropertyValue("OutputWorkspace")
_t_data = DivideMD(LHSWorkspace='_t_data', RHSWorkspace='_t_norm')
if self._bkg:
_t_bkg_data = DivideMD(LHSWorkspace='_t_bkg_data',
RHSWorkspace='_t_bkg_norm')
_t_scale = CreateSingleValuedWorkspace(DataValue=self._bkg_scale)
_t_bkg_data = MultiplyMD(_t_bkg_data, _t_scale)
ws = MinusMD(_t_data, _t_bkg_data)
RenameWorkspace(_t_data, OutputWorkspace=name + '_dat')
RenameWorkspace(_t_bkg_data, OutputWorkspace=name + '_bkg')
else:
ws = _t_data
RenameWorkspace(ws, OutputWorkspace=name)
self.setProperty("OutputWorkspace", ws)
diffraction_reporter.report(len(run_numbers), 'Done')
def PyExec(self):
# remove possible old temp workspaces
[
DeleteWorkspace(ws) for ws in self.temp_workspace_list
if mtd.doesExist(ws)
]
_background = bool(self.getProperty("Background").value)
_load_inst = bool(self.getProperty("LoadInstrument").value)
_detcal = bool(self.getProperty("DetCal").value)
_masking = bool(self.getProperty("MaskFile").value)
_outWS_name = self.getPropertyValue("OutputWorkspace")
UBList = self._generate_UBList()
dim0_min, dim0_max, dim0_bins = self.getProperty('BinningDim0').value
dim1_min, dim1_max, dim1_bins = self.getProperty('BinningDim1').value
dim2_min, dim2_max, dim2_bins = self.getProperty('BinningDim2').value
MinValues = "{},{},{}".format(dim0_min, dim1_min, dim2_min)
MaxValues = "{},{},{}".format(dim0_max, dim1_max, dim2_max)
AlignedDim0 = ",{},{},{}".format(dim0_min, dim0_max, int(dim0_bins))
AlignedDim1 = ",{},{},{}".format(dim1_min, dim1_max, int(dim1_bins))
AlignedDim2 = ",{},{},{}".format(dim2_min, dim2_max, int(dim2_bins))
LoadNexus(Filename=self.getProperty("SolidAngle").value,
OutputWorkspace='__sa')
LoadNexus(Filename=self.getProperty("Flux").value,
OutputWorkspace='__flux')
if _masking:
LoadMask(Instrument=mtd['__sa'].getInstrument().getName(),
InputFile=self.getProperty("MaskFile").value,
OutputWorkspace='__mask')
MaskDetectors(Workspace='__sa', MaskedWorkspace='__mask')
DeleteWorkspace('__mask')
XMin = mtd['__sa'].getXDimension().getMinimum()
XMax = mtd['__sa'].getXDimension().getMaximum()
if _background:
Load(Filename=self.getProperty("Background").value,
OutputWorkspace='__bkg',
FilterByTofMin=self.getProperty("FilterByTofMin").value,
FilterByTofMax=self.getProperty("FilterByTofMax").value)
if _load_inst:
LoadInstrument(
Workspace='__bkg',
Filename=self.getProperty("LoadInstrument").value,
RewriteSpectraMap=False)
if _detcal:
LoadIsawDetCal(InputWorkspace='__bkg',
Filename=self.getProperty("DetCal").value)
MaskDetectors(Workspace='__bkg', MaskedWorkspace='__sa')
ConvertUnits(InputWorkspace='__bkg',
OutputWorkspace='__bkg',
Target='Momentum')
CropWorkspace(InputWorkspace='__bkg',
OutputWorkspace='__bkg',
XMin=XMin,
XMax=XMax)
progress = Progress(
self, 0.0, 1.0,
len(UBList) * len(self.getProperty("Filename").value))
for run in self.getProperty("Filename").value:
logger.notice("Working on " + run)
Load(Filename=run,
OutputWorkspace='__run',
FilterByTofMin=self.getProperty("FilterByTofMin").value,
FilterByTofMax=self.getProperty("FilterByTofMax").value)
if _load_inst:
LoadInstrument(
Workspace='__run',
Filename=self.getProperty("LoadInstrument").value,
RewriteSpectraMap=False)
if _detcal:
LoadIsawDetCal(InputWorkspace='__run',
Filename=self.getProperty("DetCal").value)
MaskDetectors(Workspace='__run', MaskedWorkspace='__sa')
ConvertUnits(InputWorkspace='__run',
OutputWorkspace='__run',
Target='Momentum')
CropWorkspace(InputWorkspace='__run',
OutputWorkspace='__run',
XMin=XMin,
XMax=XMax)
if self.getProperty('SetGoniometer').value:
SetGoniometer(
Workspace='__run',
Goniometers=self.getProperty('Goniometers').value,
Axis0=self.getProperty('Axis0').value,
Axis1=self.getProperty('Axis1').value,
Axis2=self.getProperty('Axis2').value)
# Set background Goniometer to be the same as data
if _background:
mtd['__bkg'].run().getGoniometer().setR(
mtd['__run'].run().getGoniometer().getR())
for ub in UBList:
SetUB(Workspace='__run', UB=ub)
ConvertToMD(InputWorkspace='__run',
OutputWorkspace='__md',
QDimensions='Q3D',
dEAnalysisMode='Elastic',
Q3DFrames='HKL',
QConversionScales='HKL',
Uproj=self.getProperty('Uproj').value,
Vproj=self.getProperty('Vproj').value,
Wproj=self.getProperty('wproj').value,
MinValues=MinValues,
MaxValues=MaxValues)
MDNormSCD(
InputWorkspace=mtd['__md'],
FluxWorkspace='__flux',
SolidAngleWorkspace='__sa',
OutputWorkspace='__data',
SkipSafetyCheck=True,
TemporaryDataWorkspace='__data'
if mtd.doesExist('__data') else None,
OutputNormalizationWorkspace='__norm',
TemporaryNormalizationWorkspace='__norm'
if mtd.doesExist('__norm') else None,
AlignedDim0=mtd['__md'].getDimension(0).name + AlignedDim0,
AlignedDim1=mtd['__md'].getDimension(1).name + AlignedDim1,
AlignedDim2=mtd['__md'].getDimension(2).name + AlignedDim2)
DeleteWorkspace('__md')
if _background:
SetUB(Workspace='__bkg', UB=ub)
ConvertToMD(InputWorkspace='__bkg',
OutputWorkspace='__bkg_md',
QDimensions='Q3D',
dEAnalysisMode='Elastic',
Q3DFrames='HKL',
QConversionScales='HKL',
Uproj=self.getProperty('Uproj').value,
Vproj=self.getProperty('Vproj').value,
Wproj=self.getProperty('Wproj').value,
MinValues=MinValues,
MaxValues=MaxValues)
MDNormSCD(
InputWorkspace='__bkg_md',
FluxWorkspace='__flux',
SolidAngleWorkspace='__sa',
SkipSafetyCheck=True,
OutputWorkspace='__bkg_data',
TemporaryDataWorkspace='__bkg_data'
if mtd.doesExist('__bkg_data') else None,
OutputNormalizationWorkspace='__bkg_norm',
TemporaryNormalizationWorkspace='__bkg_norm'
if mtd.doesExist('__bkg_norm') else None,
AlignedDim0=mtd['__bkg_md'].getDimension(0).name +
AlignedDim0,
AlignedDim1=mtd['__bkg_md'].getDimension(1).name +
AlignedDim1,
AlignedDim2=mtd['__bkg_md'].getDimension(2).name +
AlignedDim2)
DeleteWorkspace('__bkg_md')
progress.report()
DeleteWorkspace('__run')
if _background:
# outWS = data / norm - bkg_data / bkg_norm * BackgroundScale
DivideMD(LHSWorkspace='__data',
RHSWorkspace='__norm',
OutputWorkspace=_outWS_name + '_normalizedData')
DivideMD(LHSWorkspace='__bkg_data',
RHSWorkspace='__bkg_norm',
OutputWorkspace=_outWS_name + '_normalizedBackground')
CreateSingleValuedWorkspace(
OutputWorkspace='__scale',
DataValue=self.getProperty('BackgroundScale').value)
MultiplyMD(LHSWorkspace=_outWS_name + '_normalizedBackground',
RHSWorkspace='__scale',
OutputWorkspace='__scaled_background')
DeleteWorkspace('__scale')
MinusMD(LHSWorkspace=_outWS_name + '_normalizedData',
RHSWorkspace='__scaled_background',
OutputWorkspace=_outWS_name)
if self.getProperty('KeepTemporaryWorkspaces').value:
RenameWorkspaces(InputWorkspaces=[
'__data', '__norm', '__bkg_data', '__bkg_norm'
],
WorkspaceNames=[
_outWS_name + '_data',
_outWS_name + '_normalization',
_outWS_name + '_background_data',
_outWS_name + '_background_normalization'
])
else:
# outWS = data / norm
DivideMD(LHSWorkspace='__data',
RHSWorkspace='__norm',
OutputWorkspace=_outWS_name)
if self.getProperty('KeepTemporaryWorkspaces').value:
RenameWorkspaces(InputWorkspaces=['__data', '__norm'],
WorkspaceNames=[
_outWS_name + '_data',
_outWS_name + '_normalization'
])
self.setProperty("OutputWorkspace", mtd[_outWS_name])
# remove temp workspaces
[
DeleteWorkspace(ws) for ws in self.temp_workspace_list
if mtd.doesExist(ws)
]
def runTest(self):
S = np.random.random(32 * 240 * 100)
ConvertWANDSCDtoQTest_data = CreateMDHistoWorkspace(
Dimensionality=3,
Extents='0.5,32.5,0.5,240.5,0.5,100.5',
SignalInput=S.ravel('F'),
ErrorInput=np.sqrt(S.ravel('F')),
NumberOfBins='32,240,100',
Names='y,x,scanIndex',
Units='bin,bin,number')
ConvertWANDSCDtoQTest_dummy = CreateSingleValuedWorkspace()
LoadInstrument(ConvertWANDSCDtoQTest_dummy,
InstrumentName='WAND',
RewriteSpectraMap=False)
ConvertWANDSCDtoQTest_data.addExperimentInfo(
ConvertWANDSCDtoQTest_dummy)
log = FloatTimeSeriesProperty('s1')
for t, v in zip(range(100), np.arange(0, 50, 0.5)):
log.addValue(t, v)
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run()['s1'] = log
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run().addProperty(
'duration', [60.] * 100, True)
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run().addProperty(
'monitor_count', [120000.] * 100, True)
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run().addProperty(
'twotheta', list(np.linspace(np.pi * 2 / 3, 0, 240).repeat(32)),
True)
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run().addProperty(
'azimuthal', list(np.tile(np.linspace(-0.15, 0.15, 32), 240)),
True)
peaks = CreatePeaksWorkspace(NumberOfPeaks=0,
OutputType='LeanElasticPeak')
SetUB(ConvertWANDSCDtoQTest_data,
5,
5,
7,
90,
90,
120,
u=[-1, 0, 1],
v=[1, 0, 1])
SetGoniometer(ConvertWANDSCDtoQTest_data,
Axis0='s1,0,1,0,1',
Average=False)
CopySample(InputWorkspace=ConvertWANDSCDtoQTest_data,
OutputWorkspace=peaks,
CopyName=False,
CopyMaterial=False,
CopyEnvironment=False,
CopyShape=False,
CopyLattice=True)
Q = ConvertWANDSCDtoQ(InputWorkspace=ConvertWANDSCDtoQTest_data,
UBWorkspace=peaks,
Wavelength=1.486,
Frame='HKL',
Uproj='1,1,0',
Vproj='-1,1,0',
BinningDim0='-6.04,6.04,151',
BinningDim1='-6.04,6.04,151',
BinningDim2='-6.04,6.04,151')
data_norm = ConvertHFIRSCDtoMDE(ConvertWANDSCDtoQTest_data,
Wavelength=1.486,
MinValues='-6.04,-6.04,-6.04',
MaxValues='6.04,6.04,6.04')
HKL = ConvertQtoHKLMDHisto(data_norm,
PeaksWorkspace=peaks,
Uproj='1,1,0',
Vproj='-1,1,0',
Extents='-6.04,6.04,-6.04,6.04,-6.04,6.04',
Bins='151,151,151')
for i in range(HKL.getNumDims()):
print(HKL.getDimension(i).getUnits(), Q.getDimension(i).getUnits())
np.testing.assert_equal(
HKL.getDimension(i).getUnits(),
Q.getDimension(i).getUnits())
hkl_data = mtd["HKL"].getSignalArray()
Q_data = mtd["Q"].getSignalArray()
print(np.isnan(Q_data).sum())
print(np.isclose(hkl_data, 0).sum())
xaxis = mtd["HKL"].getXDimension()
yaxis = mtd["HKL"].getYDimension()
zaxis = mtd["HKL"].getZDimension()
x, y, z = np.meshgrid(
np.linspace(xaxis.getMinimum(), xaxis.getMaximum(),
xaxis.getNBins()),
np.linspace(yaxis.getMinimum(), yaxis.getMaximum(),
yaxis.getNBins()),
np.linspace(zaxis.getMinimum(), zaxis.getMaximum(),
zaxis.getNBins()),
indexing="ij",
copy=False,
)
print(
x[~np.isnan(Q_data)].mean(),
y[~np.isnan(Q_data)].mean(),
z[~np.isnan(Q_data)].mean(),
)
print(
x[~np.isclose(hkl_data, 0)].mean(),
y[~np.isclose(hkl_data, 0)].mean(),
z[~np.isclose(hkl_data, 0)].mean(),
)
np.testing.assert_almost_equal(x[~np.isnan(Q_data)].mean(),
x[~np.isclose(hkl_data, 0)].mean(),
decimal=2)
np.testing.assert_almost_equal(y[~np.isnan(Q_data)].mean(),
y[~np.isclose(hkl_data, 0)].mean(),
decimal=2)
np.testing.assert_almost_equal(z[~np.isnan(Q_data)].mean(),
z[~np.isclose(hkl_data, 0)].mean(),
decimal=1)
def setUpClass(cls):
def gaussian(x, y, z, x0, y0, z0, ox, oy, oz, A):
return A * np.exp(-(x - x0)**2 / (2 * ox**2) - (y - y0)**2 /
(2 * oy**2) - (z - z0)**2 / (2 * oz**2))
def peaks(i, j, k):
return gaussian(i, j, k, 16, 100, 50, 2, 2, 2, 20) + gaussian(
i, j, k, 16, 150, 50, 1, 1, 1, 10)
S = np.fromfunction(peaks, (32, 240, 100))
ConvertWANDSCDtoQTest_data = CreateMDHistoWorkspace(
Dimensionality=3,
Extents='0.5,32.5,0.5,240.5,0.5,100.5',
SignalInput=S.ravel('F'),
ErrorInput=np.sqrt(S.ravel('F')),
NumberOfBins='32,240,100',
Names='y,x,scanIndex',
Units='bin,bin,number')
ConvertWANDSCDtoQTest_dummy = CreateSingleValuedWorkspace()
ConvertWANDSCDtoQTest_data.addExperimentInfo(
ConvertWANDSCDtoQTest_dummy)
log = FloatTimeSeriesProperty('s1')
for t, v in zip(range(100), np.arange(0, 50, 0.5)):
log.addValue(t, v)
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run()['s1'] = log
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run().addProperty(
'duration', [60.] * 100, True)
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run().addProperty(
'monitor_count', [120000.] * 100, True)
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run().addProperty(
'twotheta', list(np.linspace(np.pi * 2 / 3, 0, 240).repeat(32)),
True)
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run().addProperty(
'azimuthal', list(np.tile(np.linspace(-0.15, 0.15, 32), 240)),
True)
SetUB(ConvertWANDSCDtoQTest_data,
5,
5,
7,
90,
90,
120,
u=[-1, 0, 1],
v=[1, 0, 1])
SetGoniometer(ConvertWANDSCDtoQTest_data,
Axis0='s1,0,1,0,1',
Average=False)
# Create Normalisation workspace
S = np.ones((32, 240, 1))
ConvertWANDSCDtoQTest_norm = CreateMDHistoWorkspace(
Dimensionality=3,
Extents='0.5,32.5,0.5,240.5,0.5,1.5',
SignalInput=S,
ErrorInput=S,
NumberOfBins='32,240,1',
Names='y,x,scanIndex',
Units='bin,bin,number')
ConvertWANDSCDtoQTest_dummy2 = CreateSingleValuedWorkspace()
ConvertWANDSCDtoQTest_norm.addExperimentInfo(
ConvertWANDSCDtoQTest_dummy2)
ConvertWANDSCDtoQTest_norm.getExperimentInfo(0).run().addProperty(
'monitor_count', [100000.], True)
wavelength = data.attrs['wavelength']
s1 = data.attrs['s1']
s2 = data.attrs['s2']
detz = data.attrs['detz']
y = data["y"].value
e = data["e"].value
t1 = time.time()
CreateWorkspace(OutputWorkspace='ws',
DataX=wavelength,
DataY=y,
DataE=e,
NSpec=y.size,
UnitX='Wavelength')
SetGoniometer('ws', Axis0="{},0,1,0,1".format(s1))
AddSampleLog('ws',
LogName='HB2C:Mot:s2.RBV',
LogText=str(s2),
LogType='Number Series')
AddSampleLog('ws',
LogName='HB2C:Mot:detz.RBV',
LogText=str(detz),
LogType='Number Series')
LoadInstrument('ws', InstrumentName=instrument, RewriteSpectraMap=True)
t2 = time.time()
print("t1={}".format(t1 - t0))
print("t2={}".format(t2 - t1))
max_Q = "30"
num_peaks_to_find = 400
min_d = 8
max_d = 20
tolerance = 0.12
minVals = "-" + max_Q + ",-" + max_Q + ",-" + max_Q
maxVals = max_Q + "," + max_Q + "," + max_Q
distance_threshold = 0.9 * 6.28 / float(max_d)
for f in files:
data = Load(f)
SetGoniometer(data, Axis0="BL9:Mot:Sample:Axis1,0,1,0,1")
MDEW = ConvertToMD(InputWorkspace=data,
QDimensions="Q3D",
dEAnalysisMode="Elastic",
QConversionScales="Q in A^-1",
LorentzCorrection='0',
MinValues=minVals,
MaxValues=maxVals,
SplitInto='2',
SplitThreshold='50',
MaxRecursionDepth='11')
peaks_ws = FindPeaksMD(MDEW,
MaxPeaks=num_peaks_to_find,
PeakDistanceThreshold=distance_threshold,
AppendPeaks=True)
def runTest(self):
""" This is the old way to do MDNorm to sample, background and finally clean background from sample
Now it serves as a benchmark to generate expected result for enhanced MDNorm
Current status: To task 88 no accumulation
Next status: To task 89 no accumulation
To task 88 with accumulation
To task 89 with accumulation
"""
# Set facility that load data
config.setFacility('SNS')
Load(Filename='HYS_13656', OutputWorkspace='sum')
SetGoniometer(Workspace='sum', Axis0='s1,0,1,0,1')
# prepare sample MD: in test must between 10 and 12 to match the gold data
dgs_red_group_name1 = self.prepare_md(input_ws_name='sum',
merged_md_name='merged1',
min_log_value=10,
max_log_value=12,
log_step=LOG_VALUE_STEP,
prefix='step1')
# 1-1 Existing method (but to keep)
benchmark = self.run_old_solution('merged1',
f'{dgs_red_group_name1}_1',
'oldstep1')
self._old_clean = str(benchmark.cleaned)
# Prepare background workspace
self.prepare_single_exp_info_background(
input_md_name=f'{dgs_red_group_name1}_1',
output_md_name='bkgd_md1',
target_qframe='Q_lab')
# Test new solutions
r = MDNorm(InputWorkspace='merged1',
BackgroundWorkspace='bkgd_md1',
Dimension0Name='QDimension1',
Dimension0Binning='-5,0.05,5',
Dimension1Name='QDimension2',
Dimension1Binning='-5,0.05,5',
Dimension2Name='DeltaE',
Dimension2Binning='-2,2',
Dimension3Name='QDimension0',
Dimension3Binning='-0.5,0.5',
SymmetryOperations=SYMMETRY_OPERATION,
OutputWorkspace='clean_data',
OutputDataWorkspace='dataMD',
OutputNormalizationWorkspace='normMD',
OutputBackgroundDataWorkspace='background_dataMD',
OutputBackgroundNormalizationWorkspace='background_normMD')
# Test solution
assert hasattr(r, 'OutputBackgroundNormalizationWorkspace'
), 'MDNorm does not execute.'
# Compare workspaces: binned background data workspace
rc = CompareMDWorkspaces(Workspace1=benchmark.bkgd_data,
Workspace2='background_dataMD')
assert rc.Equals, f'Error message: {rc.Result}'
# Compare workspaces: background normalization workspace
r89 = CompareMDWorkspaces(Workspace1=benchmark.bkgd_norm,
Workspace2='background_normMD',
Tolerance=1E-9)
assert r89.Equals, f'Incompatible background normalization workspaces: {r89.Result}'
# Compare the cleaned MD
r_out = CompareMDWorkspaces(Workspace1=benchmark.cleaned,
Workspace2='clean_data',
Tolerance=1E-16)
if not r_out.Equals:
raise ValueError(
f'Sample output data does not match: {r_out.Result}')
# Round 2: Test accumulation mode
dgs_red_group_name1 = self.prepare_md(input_ws_name='sum',
merged_md_name='merged2',
min_log_value=13,
max_log_value=15,
log_step=LOG_VALUE_STEP,
prefix='step2')
# Old solution
benchmark2 = self.run_old_solution('merged2',
f'{dgs_red_group_name1}_2',
'oldstep2', benchmark)
# 1-2 Test new solutions
self.prepare_single_exp_info_background(
input_md_name=f'{dgs_red_group_name1}_2',
output_md_name='bkgd_md2',
target_qframe='Q_lab')
r = MDNorm(
InputWorkspace='merged2',
BackgroundWorkspace='bkgd_md2',
TemporaryDataWorkspace='dataMD',
TemporaryNormalizationWorkspace='normMD',
TemporaryBackgroundDataWorkspace='background_dataMD',
TemporaryBackgroundNormalizationWorkspace='background_normMD',
Dimension0Name='QDimension1',
Dimension0Binning='-5,0.05,5',
Dimension1Name='QDimension2',
Dimension1Binning='-5,0.05,5',
Dimension2Name='DeltaE',
Dimension2Binning='-2,2',
Dimension3Name='QDimension0',
Dimension3Binning='-0.5,0.5',
SymmetryOperations=SYMMETRY_OPERATION,
OutputWorkspace='clean_data_2',
OutputDataWorkspace='dataMD2',
OutputNormalizationWorkspace='normMD2',
OutputBackgroundDataWorkspace='background_dataMD2',
OutputBackgroundNormalizationWorkspace='background_normMD2')
# Compare data workspace
r_sd = CompareMDWorkspaces(Workspace1=benchmark2.sample_data,
Workspace2='dataMD',
Tolerance=1E-7)
assert r_sd.Equals, f'Sample binned data MD not equal: {r_sd.Result}'
# Compare gold (old solution) and new: passed test in #88
r = CompareMDWorkspaces(Workspace1=benchmark2.bkgd_data,
Workspace2='background_dataMD2')
if not r.Equals:
raise ValueError(
f'Accumulation mode: Background binned data MD: {r.Result}')
# Compare gold (old solution) and new: passed test in #88
r_bn = CompareMDWorkspaces(Workspace1=benchmark2.bkgd_norm,
Workspace2='background_normMD2',
Tolerance=1E-9)
if not r_bn.Equals:
raise ValueError(
f'Accumulation mode: Background normalization MD: {r_bn.Result}'
)
# Test MDNorm's checking on inputs
# Note that this test must be put after all the tests because
# it will modify some workspaces and thus mess some experimental data set up to verify
self.test_property_setup('merged1', f'{dgs_red_group_name1}_1')
def PyExec(self):
fn = self.getPropertyValue("Filename")
wsn = self.getPropertyValue("OutputWorkspace")
#print (fn, wsn)
self.fxml = self.getPropertyValue("InstrumentXML")
#load data
parms_dict, det_udet, det_count, det_tbc, data = self.read_file(fn)
nrows = int(parms_dict['NDET'])
#nbins=int(parms_dict['NTC'])
xdata = np.array(det_tbc)
xdata_mon = np.linspace(xdata[0], xdata[-1], len(xdata))
ydata = data.astype(np.float)
ydata = ydata.reshape(nrows, -1)
edata = np.sqrt(ydata)
#CreateWorkspace(OutputWorkspace=wsn,DataX=xdata,DataY=ydata,DataE=edata,
# NSpec=nrows,UnitX='TOF',WorkspaceTitle='Data',YUnitLabel='Counts')
nr, nc = ydata.shape
ws = WorkspaceFactory.create("Workspace2D",
NVectors=nr,
XLength=nc + 1,
YLength=nc)
for i in range(nrows):
ws.setX(i, xdata)
ws.setY(i, ydata[i])
ws.setE(i, edata[i])
ws.getAxis(0).setUnit('tof')
AnalysisDataService.addOrReplace(wsn, ws)
#self.setProperty("OutputWorkspace", wsn)
#print ("ws:", wsn)
#ws=mtd[wsn]
# fix the x values for the monitor
for i in range(nrows - 2, nrows):
ws.setX(i, xdata_mon)
self.log().information("set detector IDs")
#set detetector IDs
for i in range(nrows):
ws.getSpectrum(i).setDetectorID(det_udet[i])
#Sample_logs the header values are written into the sample logs
log_names = [sl.encode('ascii', 'ignore') for sl in parms_dict.keys()]
log_values = [
sl.encode('ascii', 'ignore')
if isinstance(sl, types.UnicodeType) else str(sl)
for sl in parms_dict.values()
]
AddSampleLogMultiple(Workspace=wsn,
LogNames=log_names,
LogValues=log_values)
SetGoniometer(Workspace=wsn, Goniometers='Universal')
if (self.fxml == ""):
LoadInstrument(Workspace=wsn,
InstrumentName="Exed",
RewriteSpectraMap=True)
else:
LoadInstrument(Workspace=wsn,
Filename=self.fxml,
RewriteSpectraMap=True)
RotateInstrumentComponent(
Workspace=wsn,
ComponentName='Tank',
Y=1,
Angle=-float(parms_dict['phi'].encode('ascii', 'ignore')),
RelativeRotation=False)
# Separate monitors into seperate workspace
ExtractSpectra(InputWorkspace=wsn,
WorkspaceIndexList=','.join(
[str(s) for s in range(nrows - 2, nrows)]),
OutputWorkspace=wsn + '_Monitors')
MaskDetectors(Workspace=wsn,
WorkspaceIndexList=','.join(
[str(s) for s in range(nrows - 2, nrows)]))
RemoveMaskedSpectra(InputWorkspace=wsn, OutputWorkspace=wsn)
self.setProperty("OutputWorkspace", wsn)
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])
from mantid.simpleapi import Load, SetGoniometer, ConvertToMD
import numpy as np
run1 = 'CORELLI_48505'
run2 = 'CORELLI_48513'
run1 = 'CORELLI_48508'
run2 = 'CORELLI_48516'
ws1 = Load(run1)
ws2 = Load(run2)
SetGoniometer(ws1, Axis0="BL9:Mot:Sample:Axis2,0,1,0,1")
SetGoniometer(ws2, Axis0="BL9:Mot:Sample:Axis2,0,1,0,1")
md1 = ConvertToMD(ws1,
QDimensions='Q3D',
dEAnalysisMode='Elastic',
Q3DFrames='Q_sample',
MinValues=[-10, -10, -10],
MaxValues=[10, 10, 10])
md2 = ConvertToMD(ws2,
QDimensions='Q3D',
dEAnalysisMode='Elastic',
Q3DFrames='Q_sample',
MinValues=[-10, -10, -10],
MaxValues=[10, 10, 10])
bin1 = BinMD(md1,
AlignedDim0='Q_sample_x,-10,10,1000',
AlignedDim1='Q_sample_z,-10,10,1000',
def PyExec(self):
self._load_inst = bool(self.getProperty("LoadInstrument").value)
self._apply_cal = bool(self.getProperty("ApplyCalibration").value)
self._detcal = bool(self.getProperty("DetCal").value)
self._copy_params = bool(
self.getProperty("CopyInstrumentParameters").value)
self._masking = bool(self.getProperty("MaskFile").value)
_outWS_name = self.getPropertyValue("OutputWorkspace")
_UB = bool(self.getProperty("UBMatrix").value)
self.XMin = self.getProperty("MomentumMin").value
self.XMax = self.getProperty("MomentumMax").value
MinValues = self.getProperty("MinValues").value
MaxValues = self.getProperty("MaxValues").value
if self.getProperty("OverwriteExisting").value:
if mtd.doesExist(_outWS_name):
DeleteWorkspace(_outWS_name)
progress = Progress(self, 0.0, 1.0,
len(self.getProperty("Filename").value))
for run in self.getProperty("Filename").value:
logger.notice("Working on " + run)
self.load_file_and_apply(run, '__run')
if self.getProperty('SetGoniometer').value:
SetGoniometer(
Workspace='__run',
Goniometers=self.getProperty('Goniometers').value,
Axis0=self.getProperty('Axis0').value,
Axis1=self.getProperty('Axis1').value,
Axis2=self.getProperty('Axis2').value)
if _UB:
LoadIsawUB(InputWorkspace='__run',
Filename=self.getProperty("UBMatrix").value)
if len(MinValues) == 0 or len(MaxValues) == 0:
MinValues, MaxValues = ConvertToMDMinMaxGlobal(
'__run',
dEAnalysisMode='Elastic',
Q3DFrames='Q' if self.getProperty('QFrame').value
== 'Q_sample' else 'HKL',
QDimensions='Q3D')
ConvertToMD(
InputWorkspace='__run',
OutputWorkspace=_outWS_name,
QDimensions='Q3D',
dEAnalysisMode='Elastic',
Q3DFrames=self.getProperty('QFrame').value,
QConversionScales='Q in A^-1'
if self.getProperty('QFrame').value == 'Q_sample' else 'HKL',
Uproj=self.getProperty('Uproj').value,
Vproj=self.getProperty('Vproj').value,
Wproj=self.getProperty('Wproj').value,
MinValues=MinValues,
MaxValues=MaxValues,
SplitInto=self.getProperty('SplitInto').value,
SplitThreshold=self.getProperty('SplitThreshold').value,
MaxRecursionDepth=self.getProperty('MaxRecursionDepth').value,
OverwriteExisting=False)
DeleteWorkspace('__run')
progress.report()
if mtd.doesExist('__mask'):
DeleteWorkspace('__mask')
self.setProperty("OutputWorkspace", mtd[_outWS_name])
def PyExec(self):
# Facility and database configuration
config_new_options = {
'default.facility': 'SNS',
'default.instrument': 'BASIS',
'datasearch.searcharchive': 'On'
}
# Find valid incoming momentum range
self._lambda_range = np.array(self.getProperty('LambdaRange').value)
self._momentum_range = np.sort(2 * np.pi / self._lambda_range)
# implement with ContextDecorator after python2 is deprecated)
with pyexec_setup(config_new_options) as self._temps:
# Load the mask to a temporary workspace
self._t_mask = LoadMask(
Instrument='BASIS',
InputFile=self.getProperty('MaskFile').value,
OutputWorkspace='_t_mask')
# Pre-process the background runs
if self.getProperty('BackgroundRuns').value:
bkg_run_numbers = self._getRuns(
self.getProperty('BackgroundRuns').value, doIndiv=True)
bkg_run_numbers = \
list(itertools.chain.from_iterable(bkg_run_numbers))
background_reporter = Progress(self,
start=0.0,
end=1.0,
nreports=len(bkg_run_numbers))
for i, run in enumerate(bkg_run_numbers):
if self._bkg is None:
self._bkg = self._mask_t0_crop(run, '_bkg')
self._temps.workspaces.append('_bkg')
else:
_ws = self._mask_t0_crop(run, '_ws')
self._bkg += _ws
if '_ws' not in self._temps.workspaces:
self._temps.workspaces.append('_ws')
message = 'Pre-processing background: {} of {}'.\
format(i+1, len(bkg_run_numbers))
background_reporter.report(message)
SetGoniometer(self._bkg, Axis0='0,0,1,0,1')
self._bkg_scale = self.getProperty('BackgroundScale').value
background_reporter.report(len(bkg_run_numbers), 'Done')
# Pre-process the vanadium run(s) by removing the delayed
# emission time from the moderator and then saving to file(s)
if self.getProperty('VanadiumRuns').value:
run_numbers = self._getRuns(
self.getProperty('VanadiumRuns').value, doIndiv=True)
run_numbers = list(itertools.chain.from_iterable(run_numbers))
vanadium_reporter = Progress(self,
start=0.0,
end=1.0,
nreports=len(run_numbers))
self._vanadium_files = list()
for i, run in enumerate(run_numbers):
self._vanadium_files.append(self._save_t0(run))
message = 'Pre-processing vanadium: {} of {}'. \
format(i+1, len(run_numbers))
vanadium_reporter.report(message)
vanadium_reporter.report(len(run_numbers), 'Done')
# Determination of single crystal diffraction
self._determine_single_crystal_diffraction()
LogText='%.4f' % omega_modified,
LogType='Number Series')
AddSampleLog(Workspace=event_ws,
LogName='omegaRequest',
LogText='%.4f' % omega_modified,
LogType='Number Series')
#
chi = event_ws.getRun()['chi'].value[0]
chi_modified = float(chi) - float(x_offset)
AddSampleLog(Workspace=event_ws,
LogName='chi',
LogText='%.4f' % chi_modified,
LogType='Number Series')
phi = event_ws.getRun()['phi'].value[0]
#
SetGoniometer(Workspace='event_ws', Goniometers='Universal')
print 'TOPAZ_%s Events =%20d Phi =%9.4f Chi = %9.4f Omega = %9.4f\n' % (
run, event_ws.getNumberEvents(), phi, chi_modified, omega_modified)
SaveNexus(InputWorkspace='event_ws',
Filename=os.path.join(output_directory,
instrument + "_" + run + ".nxs.h5"))
#
# Load calibration file(s) if specified. NOTE: The file name passed in to LoadIsawDetCal
# can not be None. TOPAZ has one calibration file, but SNAP may have two.
#
if (calibration_file_1 is not None) or (calibration_file_2 is not None):
if (calibration_file_1 is None):
calibration_file_1 = ""
if (calibration_file_2 is None):
def PyExec(self):
runs = self.getProperty("Filename").value
if not runs:
ipts = self.getProperty("IPTS").value
runs = [
'/HFIR/HB2C/IPTS-{}/nexus/HB2C_{}.nxs.h5'.format(ipts, run)
for run in self.getProperty("RunNumbers").value
]
wavelength = self.getProperty("wavelength").value
outWS = self.getPropertyValue("OutputWorkspace")
group_names = []
grouping = self.getProperty("Grouping").value
if grouping == 'None':
grouping = 1
else:
grouping = 2 if grouping == '2x2' else 4
for i, run in enumerate(runs):
data = np.zeros((512 * 480 * 8), dtype=np.int64)
with h5py.File(run, 'r') as f:
monitor_count = f['/entry/monitor1/total_counts'].value[0]
run_number = f['/entry/run_number'].value[0]
for b in range(8):
data += np.bincount(f['/entry/bank' + str(b + 1) +
'_events/event_id'].value,
minlength=512 * 480 * 8)
data = data.reshape((480 * 8, 512))
if grouping == 2:
data = data[::2, ::2] + data[
1::2, ::2] + data[::2, 1::2] + data[1::2, 1::2]
elif grouping == 4:
data = (data[::4, ::4] + data[1::4, ::4] + data[2::4, ::4] +
data[3::4, ::4] + data[::4, 1::4] + data[1::4, 1::4] +
data[2::4, 1::4] + data[3::4, 1::4] + data[::4, 2::4] +
data[1::4, 2::4] + data[2::4, 2::4] +
data[3::4, 2::4] + data[::4, 3::4] + data[1::4, 3::4] +
data[2::4, 3::4] + data[3::4, 3::4])
CreateWorkspace(DataX=[wavelength - 0.001, wavelength + 0.001],
DataY=data,
DataE=np.sqrt(data),
UnitX='Wavelength',
YUnitLabel='Counts',
NSpec=1966080 // grouping**2,
OutputWorkspace='__tmp_load',
EnableLogging=False)
LoadNexusLogs('__tmp_load', Filename=run, EnableLogging=False)
AddSampleLog('__tmp_load',
LogName="monitor_count",
LogType='Number',
NumberType='Double',
LogText=str(monitor_count),
EnableLogging=False)
AddSampleLog('__tmp_load',
LogName="gd_prtn_chrg",
LogType='Number',
NumberType='Double',
LogText=str(monitor_count),
EnableLogging=False)
AddSampleLog('__tmp_load',
LogName="Wavelength",
LogType='Number',
NumberType='Double',
LogText=str(wavelength),
EnableLogging=False)
AddSampleLog('__tmp_load',
LogName="Ei",
LogType='Number',
NumberType='Double',
LogText=str(
UnitConversion.run('Wavelength', 'Energy',
wavelength, 0, 0, 0, Elastic,
0)),
EnableLogging=False)
AddSampleLog('__tmp_load',
LogName="run_number",
LogText=run_number,
EnableLogging=False)
if grouping > 1: # Fix detector IDs per spectrum before loading instrument
__tmp_load = mtd['__tmp_load']
for n in range(__tmp_load.getNumberHistograms()):
s = __tmp_load.getSpectrum(n)
for i in range(grouping):
for j in range(grouping):
s.addDetectorID(
int(n * grouping % 512 + n //
(512 / grouping) * 512 * grouping + j +
i * 512))
LoadInstrument('__tmp_load',
InstrumentName='WAND',
RewriteSpectraMap=False,
EnableLogging=False)
else:
LoadInstrument('__tmp_load',
InstrumentName='WAND',
RewriteSpectraMap=True,
EnableLogging=False)
SetGoniometer('__tmp_load',
Axis0="HB2C:Mot:s1,0,1,0,1",
EnableLogging=False)
if self.getProperty("ApplyMask").value:
MaskBTP('__tmp_load', Pixel='1,2,511,512', EnableLogging=False)
if mtd['__tmp_load'].getRunNumber(
) > 26600: # They changed pixel mapping and bank name order here
MaskBTP('__tmp_load',
Bank='1',
Tube='479-480',
EnableLogging=False)
MaskBTP('__tmp_load',
Bank='8',
Tube='1-2',
EnableLogging=False)
else:
MaskBTP('__tmp_load',
Bank='8',
Tube='475-480',
EnableLogging=False)
if len(runs) == 1:
RenameWorkspace('__tmp_load', outWS, EnableLogging=False)
else:
outName = outWS + "_" + str(mtd['__tmp_load'].getRunNumber())
group_names.append(outName)
RenameWorkspace('__tmp_load', outName, EnableLogging=False)
if len(runs) > 1:
GroupWorkspaces(group_names,
OutputWorkspace=outWS,
EnableLogging=False)
self.setProperty('OutputWorkspace', 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])