def _sumForegroundInLambda(self, ws):
"""Sum the foreground region into a single histogram."""
foreground = self._foregroundIndices(ws)
sumIndices = [i for i in range(foreground[0], foreground[2] + 1)]
beamPosIndex = foreground[1]
foregroundWSName = self._names.withSuffix('foreground_grouped')
foregroundWS = ExtractSingleSpectrum(InputWorkspace=ws,
OutputWorkspace=foregroundWSName,
WorkspaceIndex=beamPosIndex,
EnableLogging=self._subalgLogging)
maxIndex = ws.getNumberHistograms() - 1
foregroundYs = foregroundWS.dataY(0)
foregroundEs = foregroundWS.dataE(0)
numpy.square(foregroundEs, out=foregroundEs)
for i in sumIndices:
if i == beamPosIndex:
continue
if i < 0 or i > maxIndex:
self.log().warning(
'Foreground partially out of the workspace.')
ys = ws.readY(i)
foregroundYs += ys
es = ws.readE(i)
foregroundEs += es**2
numpy.sqrt(foregroundEs, out=foregroundEs)
self._cleanup.cleanup(ws)
AddSampleLog(Workspace=foregroundWS,
LogName=common.SampleLogs.SUM_TYPE,
LogText=SumType.IN_LAMBDA,
LogType='String',
EnableLogging=self._subalgLogging)
ConvertToDistribution(Workspace=foregroundWS,
EnableLogging=self._subalgLogging)
return foregroundWS
def _compute_caad(fit_workspaces):
indices = _get_caad_indices(fit_workspaces[0])
normalised_workspaces = [
_normalise_by_integral(_extract_spectra(workspace, indices))
for workspace in fit_workspaces
]
number_of_spectrum = normalised_workspaces[0].getNumberHistograms()
normalised_workspaces = [[
ExtractSingleSpectrum(InputWorkspace=workspace,
OutputWorkspace="__extracted",
WorkspaceIndex=index,
StoreInADS=False,
EnableLogging=True)
for workspace in normalised_workspaces
] for index in range(number_of_spectrum)]
normalised_workspaces = [
_append_all(workspaces) for workspaces in normalised_workspaces
]
summed_workspaces = [
SumSpectra(InputWorkspace=workspace,
OutputWorkspace="__summed",
StoreInADS=False,
EnableLogging=False) for workspace in normalised_workspaces
]
return normalised_workspaces, summed_workspaces, indices
def monitorTransfit(self, files, foilType, divE):
isFirstFile = True
isSingleFile = len(files) == 1
firstFileName = ""
for file in files:
discard, fileName = path.split(file)
fnNoExt = path.splitext(fileName)[0]
if isFirstFile:
firstFileName = fnNoExt
fileName_Raw = fnNoExt + '_raw'
fileName_3 = fnNoExt + '_3'
LoadRaw(Filename=file, OutputWorkspace=fileName_Raw)
CropWorkspace(InputWorkspace=fileName_Raw, OutputWorkspace=fileName_Raw, XMin=100, XMax=19990)
NormaliseByCurrent(InputWorkspace=fileName_Raw, OutputWorkspace=fileName_Raw)
ExtractSingleSpectrum(InputWorkspace=fileName_Raw, OutputWorkspace=fileName_3, WorkspaceIndex=3)
DeleteWorkspace(fileName_Raw)
ConvertUnits(InputWorkspace=fileName_3, Target='Energy', OutputWorkspace=fileName_3)
self.TransfitRebin(fileName_3, fileName_3, foilType, divE)
if not isFirstFile:
Plus(LHSWorkspace=firstFileName + '_3', RHSWorkspace=fileName_3, OutputWorkspace=firstFileName + '_3')
DeleteWorkspace(fileName_3)
else:
isFirstFile = False
if isSingleFile:
RenameWorkspace(InputWorkspace=firstFileName + '_3', OutputWorkspace=firstFileName + '_monitor')
else:
noFiles = len(files) ** (-1)
CreateSingleValuedWorkspace(OutputWorkspace='scale', DataValue=noFiles)
Multiply(LHSWorkspace=firstFileName + '_3', RHSWorkspace='scale',
OutputWorkspace=firstFileName + '_monitor')
DeleteWorkspace('scale')
DeleteWorkspace(firstFileName + '_3')
def crop_workspaces(workspace_names, spec_min, spec_max, extract_monitors=True, monitor_index=0):
"""
Crops the workspaces with the specified workspace names, from the specified minimum
spectra to the specified maximum spectra.
:param workspace_names: The names of the workspaces to crop
:param spec_min: The minimum spectra of the cropping region
:param spec_max: The maximum spectra of the cropping region
:param extract_monitors: If True, extracts monitors from the workspaces
:param monitor_index: The index of the monitors in the workspaces
"""
from mantid.simpleapi import ExtractSingleSpectrum, CropWorkspace
for workspace_name in workspace_names:
if extract_monitors:
# Get the monitor spectrum
monitor_ws_name = workspace_name + '_mon'
ExtractSingleSpectrum(InputWorkspace=workspace_name,
OutputWorkspace=monitor_ws_name,
WorkspaceIndex=monitor_index)
# Crop to the detectors required
workspace = mtd[workspace_name]
CropWorkspace(InputWorkspace=workspace_name,
OutputWorkspace=workspace_name,
StartWorkspaceIndex=workspace.getIndexFromSpectrumNumber(int(spec_min)),
EndWorkspaceIndex=workspace.getIndexFromSpectrumNumber(int(spec_max)))
def PyExec(self):
# get parameter values
wsString = self.getPropertyValue("InputWorkspace").strip()
#internal values
wsOutput = "__OutputWorkspace"
wsTemp = "__Sort_temp"
#get the workspace list
wsNames = []
for wsName in wsString.split(","):
ws = mtd[wsName.strip()]
if type(ws) == WorkspaceGroup:
wsNames.extend(ws.getNames())
else:
wsNames.append(wsName)
if wsOutput in mtd:
DeleteWorkspace(Workspace=wsOutput)
sortStat = []
for wsName in wsNames:
if "qvectors" in wsName:
#extract the spectrum
ws = mtd[wsName.strip()]
for s in range(0, ws.getNumberHistograms()):
y_s = ws.readY(s)
tuple = (self.GetXValue(y_s), s)
sortStat.append(tuple)
sortStat.sort()
if len(sortStat) == 0:
raise RuntimeError("Cannot find file with qvectors, aborting")
#sort spectra using norm of q
for wsName in wsNames:
ws = mtd[wsName.strip()]
yUnit = ws.getAxis(1).getUnit().unitID()
transposed = False
if ws.getNumberHistograms() < len(sortStat):
Transpose(InputWorkspace=wsName, OutputWorkspace=wsName)
transposed = True
for norm, spec in sortStat:
ExtractSingleSpectrum(InputWorkspace=wsName, OutputWorkspace=wsTemp, WorkspaceIndex=spec)
if wsOutput in mtd:
ConjoinWorkspaces(InputWorkspace1=wsOutput,InputWorkspace2=wsTemp,CheckOverlapping=False)
if wsTemp in mtd:
DeleteWorkspace(Workspace=wsTemp)
else:
RenameWorkspace(InputWorkspace=wsTemp, OutputWorkspace=wsOutput)
#put norm as y value and copy units from input
loopIndex = 0
wsOut = mtd[wsOutput]
for norm, spec in sortStat:
wsOut.getSpectrum(loopIndex).setSpectrumNo(int(norm*1000))
loopIndex = loopIndex + 1
if len(yUnit) > 0:
wsOut.getAxis(1).setUnit(yUnit)
if transposed:
Transpose(InputWorkspace=wsOutput, OutputWorkspace=wsOutput)
RenameWorkspace(InputWorkspace=wsOutput, OutputWorkspace=wsName)
def _sumForegroundInLambda(self, ws):
"""Sum the foreground region into a single histogram."""
foreground = self._foregroundIndices(ws)
sumIndices = [i for i in range(foreground[0], foreground[2] + 1)]
beamPosIndex = foreground[1]
foregroundWSName = self._names.withSuffix('foreground_grouped')
foregroundWS = ExtractSingleSpectrum(
InputWorkspace=ws,
OutputWorkspace=foregroundWSName,
WorkspaceIndex=beamPosIndex,
EnableLogging=self._subalgLogging)
maxIndex = ws.getNumberHistograms() - 1
foregroundYs = foregroundWS.dataY(0)
foregroundEs = foregroundWS.dataE(0)
numpy.square(foregroundEs, out=foregroundEs)
for i in sumIndices:
if i == beamPosIndex:
continue
if i < 0 or i > maxIndex:
self.log().warning('Foreground partially out of the workspace.')
addeeWSName = self._names.withSuffix('foreground_addee')
addeeWS = ExtractSingleSpectrum(
InputWorkspace=ws,
OutputWorkspace=addeeWSName,
WorkspaceIndex=i,
EnableLogging=self._subalgLogging)
addeeWS = RebinToWorkspace(
WorkspaceToRebin=addeeWS,
WorkspaceToMatch=foregroundWS,
OutputWorkspace=addeeWSName,
EnableLogging=self._subalgLogging)
ys = addeeWS.readY(0)
foregroundYs += ys
es = addeeWS.readE(0)
foregroundEs += es**2
self._cleanup.cleanup(addeeWS)
self._cleanup.cleanup(ws)
numpy.sqrt(foregroundEs, out=foregroundEs)
return foregroundWS
def int3samples(runs, name, masks, binning='0.5, 0.05, 8.0'):
"""
Finds the polarisation versus wavelength for a set of detector tubes.
Parameters
----------
runs: list of RunData objects
The runs whose polarisation we are interested in.
name: string
The name of this set of runs
masks: list of string
The file names of the masks for the sequential tubes that are being used
for the SEMSANS measurements.
binning: string
The binning values to use for the wavelength bins. The default value is
'0.5, 0.025, 10.0'
"""
for tube, _ in enumerate(masks):
for i in [1, 2]:
final_state = "{}_{}_{}".format(name, tube, i)
if final_state in mtd.getObjectNames():
DeleteWorkspace(final_state)
for rnum in runs:
w1 = Load(BASE.format(rnum.number), LoadMonitors=True)
w1mon = ExtractSingleSpectrum('w1_monitors', 0)
w1 = ConvertUnits('w1', 'Wavelength', AlignBins=1)
w1mon = ConvertUnits(w1mon, 'Wavelength')
w1 = Rebin(w1, binning, PreserveEvents=False)
w1mon = Rebin(w1mon, binning)
w1 = w1 / w1mon
for tube, mask in enumerate(masks):
Mask_Tube = LoadMask('LARMOR', mask)
w1temp = CloneWorkspace(w1)
MaskDetectors(w1temp, MaskedWorkspace="Mask_Tube")
Tube_Sum = SumSpectra(w1temp)
for i in [1, 2]:
final_state = "{}_{}_{}".format(name, tube, i)
if final_state in mtd.getObjectNames():
mtd[final_state] += mtd["Tube_Sum_{}".format(i)]
else:
mtd[final_state] = mtd["Tube_Sum_{}".format(i)]
x = mtd["{}_0_1".format(name)].extractX()[0]
dx = (x[1:] + x[:-1]) / 2
pols = []
for run in runs:
he_stat = he3_stats(run)
start = (run.start - he_stat.dt).seconds / 3600 / he_stat.t1
end = (run.end - he_stat.dt).seconds / 3600 / he_stat.t1
for time in np.linspace(start, end, 10):
temp = he3pol(he_stat.scale, time)(dx)
pols.append(temp)
wpol = CreateWorkspace(
x,
np.mean(pols, axis=0),
# and the blank
UnitX="Wavelength",
YUnitLabel="Counts")
for tube, _ in enumerate(masks):
up = mtd["{}_{}_2".format(name, tube)]
dn = mtd["{}_{}_1".format(name, tube)]
pol = (up - dn) / (up + dn)
pol /= wpol
DeleteWorkspaces(
["{}_{}_{}".format(name, tube, i) for i in range(1, 3)])
RenameWorkspace("pol", OutputWorkspace="{}_{}".format(name, tube))
DeleteWorkspaces(["Tube_Sum_1", "Tube_Sum_2"])
GroupWorkspaces([
"{}_{}".format(name, tube) for tube, _ in enumerate(masks)
for i in range(1, 3)
],
OutputWorkspace=str(name))
WorkspaceIndex=id_slice,
StartX=start,
EndX=end,
Output="{0}{1}".format(out_root, id_slice),
CreateOutput=True)
for key in ("Workspace", "Parameters", "NormalisedCovarianceMatrix"):
workspaces_to_group.append("{0}{1}_{2}".format(out_root, id_slice,
key))
# Update the model string. We take the current optimized model
# as the initial guess for fitting of the next slice.
parameters_workspace = mtd["{0}{1}_Parameters".format(out_root, id_slice)]
model_string = update_model(model_string, parameters_workspace)
# Calculate the PDF of the residuals, which is the slice without
# the model background
fits_workspace = mtd["{0}{1}_Workspace".format(out_root, id_slice)]
residuals = ExtractSingleSpectrum(fits_workspace, 2)
sqe = CloneWorkspace(residuals, OutputWorkspace=out_sqe)\
if not sqe else append_spectrum(residuals, sqe)
single_gre = PDFFourierTransform(InputWorkspace=residuals,
InputSofQType="S(Q)-1",
Qmin=start,
Qmax=end,
PDFType="G(r)",
DeltaR=gre_options["DeltaR"],
Rmax=gre_options["Rmax"])
gre = CloneWorkspace(single_gre, OutputWorkspace=out_gre)\
if not gre else append_spectrum(single_gre, gre)
id_slice += jump
# Group all the fit workspaces
GroupWorkspaces(InputWorkspaces=",".join(workspaces_to_group),
def load_files(data_files, ipf_filename, spec_min, spec_max, sum_files=False, load_logs=True, load_opts=None):
"""
Loads a set of files and extracts just the spectra we care about (i.e. detector range and monitor).
@param data_files List of data file names
@param ipf_filename File path/name for the instrument parameter file to load
@param spec_min Minimum spectra ID to load
@param spec_max Maximum spectra ID to load
@param sum_files Sum loaded files
@param load_logs Load log files when loading runs
@param load_opts Additional options to be passed to load algorithm
@return List of loaded workspace names and flag indicating chopped data
"""
from mantid.simpleapi import (Load, LoadVesuvio, LoadParameterFile,
ChopData, ExtractSingleSpectrum,
CropWorkspace)
if load_opts is None:
load_opts = {}
workspace_names = []
for filename in data_files:
# The filename without path and extension will be the workspace name
ws_name = os.path.splitext(os.path.basename(str(filename)))[0]
logger.debug('Loading file %s as workspace %s' % (filename, ws_name))
if 'VESUVIO' in ipf_filename:
# Load all spectra. They are cropped later
LoadVesuvio(Filename=str(filename),
OutputWorkspace=ws_name,
SpectrumList='1-198',
**load_opts)
else:
Load(Filename=filename,
OutputWorkspace=ws_name,
LoadLogFiles=load_logs,
**load_opts)
# Load the instrument parameters
LoadParameterFile(Workspace=ws_name,
Filename=ipf_filename)
# Add the workspace to the list of workspaces
workspace_names.append(ws_name)
# Get the spectrum number for the monitor
instrument = mtd[ws_name].getInstrument()
monitor_index = int(instrument.getNumberParameter('Workflow.Monitor1-SpectrumNumber')[0])
logger.debug('Workspace %s monitor 1 spectrum number :%d' % (ws_name, monitor_index))
# Chop data if required
try:
chop_threshold = mtd[ws_name].getInstrument().getNumberParameter('Workflow.ChopDataIfGreaterThan')[0]
x_max = mtd[ws_name].readX(0)[-1]
chopped_data = x_max > chop_threshold
except IndexError:
chopped_data = False
logger.information('Workspace {0} need data chop: {1}'.format(ws_name, str(chopped_data)))
workspaces = [ws_name]
if chopped_data:
ChopData(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
MonitorWorkspaceIndex=monitor_index,
IntegrationRangeLower=5000.0,
IntegrationRangeUpper=10000.0,
NChops=5)
workspaces = mtd[ws_name].getNames()
for chop_ws_name in workspaces:
# Get the monitor spectrum
monitor_ws_name = chop_ws_name + '_mon'
ExtractSingleSpectrum(InputWorkspace=chop_ws_name,
OutputWorkspace=monitor_ws_name,
WorkspaceIndex=monitor_index)
# Crop to the detectors required
chop_ws = mtd[chop_ws_name]
CropWorkspace(InputWorkspace=chop_ws_name,
OutputWorkspace=chop_ws_name,
StartWorkspaceIndex=chop_ws.getIndexFromSpectrumNumber(int(spec_min)),
EndWorkspaceIndex=chop_ws.getIndexFromSpectrumNumber(int(spec_max)))
logger.information('Loaded workspace names: %s' % (str(workspace_names)))
logger.information('Chopped data: %s' % (str(chopped_data)))
# Sum files if needed
if sum_files and len(data_files) > 1:
if chopped_data:
workspace_names = sum_chopped_runs(workspace_names)
else:
workspace_names = sum_regular_runs(workspace_names)
logger.information('Summed workspace names: %s' % (str(workspace_names)))
return workspace_names, chopped_data
def _sumForegroundInLambda(self, ws):
"""Sum the foreground region into a single histogram."""
foreground = self._foregroundIndices(ws)
sumIndices = [i for i in range(foreground[0], foreground[2] + 1)]
beamPosIndex = foreground[1]
foregroundWSName = self._names.withSuffix('grouped')
foregroundWS = ExtractSingleSpectrum(InputWorkspace=ws,
OutputWorkspace=foregroundWSName,
WorkspaceIndex=beamPosIndex,
EnableLogging=self._subalgLogging)
maxIndex = ws.getNumberHistograms() - 1
foregroundYs = foregroundWS.dataY(0)
foregroundEs = foregroundWS.dataE(0)
numpy.square(foregroundEs, out=foregroundEs)
for i in sumIndices:
if i == beamPosIndex:
continue
if i < 0 or i > maxIndex:
self.log().warning(
'Foreground partially out of the workspace.')
addeeWSName = self._names.withSuffix('addee')
addeeWS = ExtractSingleSpectrum(InputWorkspace=ws,
OutputWorkspace=addeeWSName,
WorkspaceIndex=i,
EnableLogging=self._subalgLogging)
addeeWS = RebinToWorkspace(WorkspaceToRebin=addeeWS,
WorkspaceToMatch=foregroundWS,
OutputWorkspace=addeeWSName,
EnableLogging=self._subalgLogging)
ys = addeeWS.readY(0)
foregroundYs += ys
es = addeeWS.readE(0)
foregroundEs += es**2
self._cleanup.cleanup(addeeWS)
self._cleanup.cleanup(ws)
numpy.sqrt(foregroundEs, out=foregroundEs)
# Move the detector to the fractional linePosition
linePosition = ws.run().getProperty(
common.SampleLogs.LINE_POSITION).value
instr = common.instrumentName(ws)
pixelSize = common.pixelSize(instr)
dist = pixelSize * (linePosition - beamPosIndex)
if dist != 0.:
detPoint1 = ws.spectrumInfo().position(0)
detPoint2 = ws.spectrumInfo().position(20)
beta = numpy.math.atan2((detPoint2[0] - detPoint1[0]),
(detPoint2[2] - detPoint1[2]))
xvsy = numpy.math.sin(beta) * dist
mz = numpy.math.cos(beta) * dist
if instr == 'D17':
mx = xvsy
my = 0.0
rotationAxis = [0, 1, 0]
else:
mx = 0.0
my = xvsy
rotationAxis = [-1, 0, 0]
MoveInstrumentComponent(Workspace=foregroundWS,
ComponentName='detector',
X=mx,
Y=my,
Z=mz,
RelativePosition=True)
theta = foregroundWS.spectrumInfo().twoTheta(0) / 2.
RotateInstrumentComponent(Workspace=foregroundWS,
ComponentName='detector',
X=rotationAxis[0],
Y=rotationAxis[1],
Z=rotationAxis[2],
Angle=theta,
RelativeRotation=True)
return foregroundWS
def calculate_ei(input_file):
# Auto Find Eis by finding maximum data point in m2 (excluding end points)
# and looking for neighbouring reps according to Fermi speed
# Doesn't deal with cases where the peaks enter the 2nd frame (i.e 2 meV on MARI)
print(input_file)
w1 = Load(input_file)
mon = LoadNexusMonitors(input_file)
run = w1.getRun()
# set up ==================================================
monitor_spectra_2 = 41475
monitor_spectra_3 = 41476
monitor_index_2 = 2
monitor_index_3 = 3
log = 'Fermi_Speed'
# Get instrument parameters ===============================
inst = w1.getInstrument()
source = inst.getSource()
L_m2 = mon.getDetector(monitor_index_2).getDistance(source)
L_m3 = mon.getDetector(monitor_index_3).getDistance(source)
L_Fermi = inst.getComponentByName("chopper-position").getDistance(source)
freq = run.getLogData(log).value[-1]
period = L_m2 / L_Fermi * 1.e6 / freq / 2. # include pi-pulses
# Find maximum value and identify strongest rep ===========
m2spec = ExtractSingleSpectrum(mon,monitor_index_2)
m2spec = Rebin(m2spec,"200,2,18000")
maxm2 = Max(m2spec)
TOF = maxm2.readX(0)[0]
# Generate list of possible reps in m2 ====================
irep = -5
while True:
t = TOF + irep*period
if t > 0:
ireps = numpy.array(range(irep,irep+20))
reps = TOF + period * ireps
break
else:
irep += 1
# exclude all reps that go past the frame in m3 ===========
reps_m3 = reps * L_m3 / L_m2
reps_m3 = [x for x in reps_m3 if x < 19999.]
reps = reps[0:len(reps_m3)]
# exclude all reps at short times
reps = [x for x in reps if x > 200.]
# try GetEi for the reps ==================================
Ei = []
TOF = []
for t in reps:
v_i = L_m2 / t # m/mus
Ei_guess = 5.227e6 * v_i**2 # meV
try:
(En,TOF2,dummy,tzero) = GetEi(mon, monitor_spectra_2, monitor_spectra_3, Ei_guess)
except:
continue
if abs(t - TOF2) > 20. or abs(tzero) > 100.: continue
Ei.append(Ei_guess)
TOF.append(TOF2)
#=========================================================
for ii in range(len(Ei)):
print("%f meV at TOF = %f mus" % (Ei[ii],TOF[ii]))
return Ei
class VesuvioCorrections(VesuvioBase):
_input_ws = None
_output_ws = None
_correction_workspaces = None
_linear_fit_table = None
#------------------------------------------------------------------------------
def summary(self):
return "Apply post fitting steps to vesuvio data"
#------------------------------------------------------------------------------
def PyInit(self):
# Inputs
self.declareProperty(MatrixWorkspaceProperty("InputWorkspace", "", direction=Direction.Input),
doc="Input TOF workspace")
self.declareProperty("WorkspaceIndex", 0,
doc="Index of spectrum to calculate corrections for")
self.declareProperty(ITableWorkspaceProperty("FitParameters", "", direction=Direction.Input,
optional=PropertyMode.Optional),
doc="Table containing the calculated fit parameters for the data in the workspace")
float_length_validator = FloatArrayLengthValidator()
float_length_validator.setLengthMin(1)
self.declareProperty(FloatArrayProperty("Masses", float_length_validator),
doc="Mass values for fitting")
self.declareProperty("MassProfiles", "", StringMandatoryValidator(),
doc="Functions used to approximate mass profile. "
"The format is function=Function1Name,param1=val1,param2=val2;function=Function2Name,param3=val3,param4=val4")
self.declareProperty("IntensityConstraints", "",
doc="A semi-colon separated list of intensity constraints defined as lists e.g "
"[0,1,0,-4];[1,0,-2,0]")
# Container
self.declareProperty(MatrixWorkspaceProperty("ContainerWorkspace", "", direction=Direction.Input,
optional=PropertyMode.Optional),
doc="Container workspace in TOF")
self.declareProperty("ContainerScale", 0.0,
doc="Scale factor to apply to container, set to 0 for automatic scale based on linear fit")
# Gamma background
self.declareProperty("GammaBackground", True, direction=Direction.Input,
doc="If true, correct for the gamma background")
self.declareProperty("GammaBackgroundScale", 0.0,
doc="Scale factor to apply to gamma background, set to 0 for automatic scale based on linear fit")
# Multiple scattering
self.declareProperty("MultipleScattering", True, direction=Direction.Input,
doc="If true, correct for the effects of multiple scattering")
self.declareProperty("BeamRadius", 2.5,
doc="Radius of beam in cm")
self.declareProperty("SampleHeight", 5.0,
doc="Height of sample in cm")
self.declareProperty("SampleWidth", 5.0,
doc="Width of sample in cm")
self.declareProperty("SampleDepth", 5.0,
doc="Depth of sample in cm")
self.declareProperty("SampleDensity", 1.0,
doc="Sample density in g/cm^3")
self.declareProperty("Seed", 123456789,
doc="")
self.declareProperty("NumScatters", 3,
doc="")
self.declareProperty("NumRuns", 10,
doc="")
self.declareProperty("NumEvents", 50000,
doc="Number of neutron events")
self.declareProperty("SmoothNeighbours", 3,
doc="")
# Outputs
self.declareProperty(WorkspaceGroupProperty("CorrectionWorkspaces", "",
direction=Direction.Output,
optional=PropertyMode.Optional),
doc="Workspace group containing correction intensities for each correction")
self.declareProperty(WorkspaceGroupProperty("CorrectedWorkspaces", "",
direction=Direction.Output,
optional=PropertyMode.Optional),
doc="Workspace group containing individual corrections applied to raw data")
self.declareProperty(ITableWorkspaceProperty("LinearFitResult", "",
direction=Direction.Output,
optional=PropertyMode.Optional),
doc="Table workspace containing the fit parameters used to"
"linearly fit the corrections to the data")
self.declareProperty(MatrixWorkspaceProperty("OutputWorkspace", "", direction=Direction.Output),
doc="The name of the output workspace")
#------------------------------------------------------------------------------
def validateInputs(self):
errors = dict()
if self.getProperty("FitParameters").value is None:
errors["FitParameters"] = "Corrections require a set of parameters from a fit of the data"
return errors
#------------------------------------------------------------------------------
def PyExec(self):
from mantid.simpleapi import (ExtractSingleSpectrum, GroupWorkspaces,
Scale, Minus, DeleteWorkspace)
self._input_ws = self.getPropertyValue("InputWorkspace")
container_ws = self.getPropertyValue("ContainerWorkspace")
spec_idx = self.getProperty("WorkspaceIndex").value
self._output_ws = self.getPropertyValue("OutputWorkspace")
self._correction_wsg = self.getPropertyValue("CorrectionWorkspaces")
self._corrected_wsg = self.getPropertyValue("CorrectedWorkspaces")
self._linear_fit_table = self.getPropertyValue("LinearFitResult")
ExtractSingleSpectrum(InputWorkspace=self._input_ws,
OutputWorkspace=self._output_ws,
WorkspaceIndex=spec_idx)
self._correction_workspaces = list()
self._container_ws = None
if container_ws != "":
container_name = str(self._correction_wsg) + "_Container"
self._container_ws = ExtractSingleSpectrum(InputWorkspace=container_ws,
OutputWorkspace=container_name,
WorkspaceIndex=spec_idx)
self._correction_workspaces.append(self._container_ws.name())
# Do gamma correction
if self.getProperty("GammaBackground").value:
self._correction_workspaces.append(self._gamma_correction())
# Do multiple scattering correction
if self.getProperty("MultipleScattering").value:
self._correction_workspaces.extend(self._ms_correction())
# Output correction workspaces as a WorkspaceGroup
if self._correction_wsg != "":
GroupWorkspaces(InputWorkspaces=self._correction_workspaces,
OutputWorkspace=self._correction_wsg)
self.setProperty("CorrectionWorkspaces", self._correction_wsg)
# Perform fitting to obtain scale factors for corrections
# The workspaces to fit for correction scale factors
fit_corrections = [wks for wks in self._correction_workspaces if 'MultipleScattering' not in wks]
# Perform fitting of corrections
fixed_params = {}
fixed_gamma_factor = self.getProperty("GammaBackgroundScale").value
if fixed_gamma_factor != 0.0:
fixed_params['GammaBackground'] = fixed_gamma_factor
fixed_container_scale = self.getProperty("ContainerScale").value
if fixed_container_scale != 0.0:
fixed_params['Container'] = fixed_container_scale
params_ws = self._fit_corrections(fit_corrections, self._linear_fit_table, **fixed_params)
self.setProperty("LinearFitResult", params_ws)
# Scale gamma background
if self.getProperty("GammaBackground").value:
gamma_correct_ws = self._get_correction_workspace('GammaBackground')[1]
gamma_factor = self._get_correction_scale_factor('GammaBackground', fit_corrections, params_ws)
Scale(InputWorkspace=gamma_correct_ws,
OutputWorkspace=gamma_correct_ws,
Factor=gamma_factor)
# Scale multiple scattering
if self.getProperty("MultipleScattering").value:
# Use factor of total scattering as this includes single and multiple scattering
multi_scatter_correct_ws = self._get_correction_workspace('MultipleScattering')[1]
total_scatter_correct_ws = self._get_correction_workspace('TotalScattering')[1]
total_scatter_factor = self._get_correction_scale_factor('TotalScattering', fit_corrections, params_ws)
Scale(InputWorkspace=multi_scatter_correct_ws,
OutputWorkspace=multi_scatter_correct_ws,
Factor=total_scatter_factor)
Scale(InputWorkspace=total_scatter_correct_ws,
OutputWorkspace=total_scatter_correct_ws,
Factor=total_scatter_factor)
# Scale by container
if container_ws != "":
container_correct_ws = self._get_correction_workspace('Container')[1]
container_factor = self._get_correction_scale_factor('Container', fit_corrections, params_ws)
Scale(InputWorkspace=container_correct_ws,
OutputWorkspace=container_correct_ws,
Factor=container_factor)
# Calculate and output corrected workspaces as a WorkspaceGroup
if self._corrected_wsg != "":
corrected_workspaces = [ws_name.replace(self._correction_wsg, self._corrected_wsg) for ws_name in self._correction_workspaces]
for corrected, correction in zip(corrected_workspaces, self._correction_workspaces):
Minus(LHSWorkspace=self._output_ws,
RHSWorkspace=correction,
OutputWorkspace=corrected)
GroupWorkspaces(InputWorkspaces=corrected_workspaces,
OutputWorkspace=self._corrected_wsg)
self.setProperty("CorrectedWorkspaces", self._corrected_wsg)
# Apply corrections
for correction in self. _correction_workspaces:
if 'TotalScattering' not in correction:
Minus(LHSWorkspace=self._output_ws,
RHSWorkspace=correction,
OutputWorkspace=self._output_ws)
self.setProperty("OutputWorkspace", self._output_ws)
# Remove correction workspaces if they are no longer required
if self._correction_wsg == "":
for wksp in self._correction_workspaces:
DeleteWorkspace(wksp)
#------------------------------------------------------------------------------
def _fit_corrections(self, fit_workspaces, param_table_name, **fixed_parameters):
func_template = Template("name=TabulatedFunction,Workspace=$ws_name,ties=(${scale_tie}Shift=0,XScaling=1),constraints=(Scaling>=0.0)")
functions = []
for idx, wsn in enumerate(fit_workspaces):
tie = ''
for param, value in fixed_parameters.iteritems():
if param in wsn:
tie = 'Scaling=%f,' % value
functions.append(func_template.substitute(ws_name=wsn, scale_tie=tie))
logger.notice('Corrections scale fit index %d is %s' % (idx, wsn))
fit = AlgorithmManager.create("Fit")
fit.initialize()
fit.setChild(True)
fit.setLogging(True)
fit.setProperty("Function", ";".join(functions))
fit.setProperty("InputWorkspace", self._input_ws)
fit.setProperty("Output", param_table_name)
fit.setProperty("CreateOutput", True)
fit.execute()
return fit.getProperty('OutputParameters').value
#------------------------------------------------------------------------------
def _get_correction_workspace(self, correction_name, corrections=None):
if corrections is None:
corrections = self._correction_workspaces
for idx, ws_name in enumerate(corrections):
if correction_name in ws_name:
return idx, ws_name
return None, None
#------------------------------------------------------------------------------
def _get_correction_scale_factor(self, correction_name, corrections, params_ws):
index = self._get_correction_workspace(correction_name, corrections)[0]
if index is None:
raise RuntimeError('No workspace for given correction')
params_dict = TableWorkspaceDictionaryFacade(params_ws)
scale_param_name = 'f%d.Scaling' % index
return params_dict[scale_param_name]
#------------------------------------------------------------------------------
def _gamma_correction(self):
from mantid.simpleapi import (CalculateGammaBackground, CloneWorkspace,
DeleteWorkspace)
correction_background_ws = str(self._correction_wsg) + "_GammaBackground"
fit_opts = parse_fit_options(mass_values=self.getProperty("Masses").value,
profile_strs=self.getProperty("MassProfiles").value,
constraints_str=self.getProperty("IntensityConstraints").value)
params_ws_name = self.getPropertyValue("FitParameters")
params_dict = TableWorkspaceDictionaryFacade(mtd[params_ws_name])
func_str = fit_opts.create_function_str(params_dict)
CalculateGammaBackground(InputWorkspace=self._output_ws,
ComptonFunction=func_str,
BackgroundWorkspace=correction_background_ws,
CorrectedWorkspace='__corrected_dummy')
DeleteWorkspace('__corrected_dummy')
return correction_background_ws
#------------------------------------------------------------------------------
def _ms_correction(self):
"""
Calculates the contributions from multiple scattering
on the input data from the set of given options
"""
from mantid.simpleapi import (CalculateMSVesuvio, CreateSampleShape,
DeleteWorkspace, SmoothData, Minus)
masses = self.getProperty("Masses").value
params_ws_name = self.getPropertyValue("FitParameters")
params_dict = TableWorkspaceDictionaryFacade(mtd[params_ws_name])
atom_props = list()
for i, mass in enumerate(masses):
intentisty_prop = 'f%d.Intensity' % i
c0_prop = 'f%d.C_0' % i
if intentisty_prop in params_dict:
intentisy = params_dict[intentisty_prop]
elif c0_prop in params_dict:
intentisy = params_dict[c0_prop]
else:
continue
width = params_dict['f%d.Width' % i]
atom_props.append(mass)
atom_props.append(intentisy)
atom_props.append(width)
# Create the sample shape
# Input dimensions are expected in CM
CreateSampleShape(InputWorkspace=self._output_ws,
ShapeXML=create_cuboid_xml(self.getProperty("SampleHeight").value/100.,
self.getProperty("SampleWidth").value/100.,
self.getProperty("SampleDepth").value/100.))
# Massage options into how algorithm expects them
total_scatter_correction = str(self._correction_wsg) + "_TotalScattering"
multi_scatter_correction = str(self._correction_wsg) + "_MultipleScattering"
# Calculation
CalculateMSVesuvio(InputWorkspace=self._output_ws,
NoOfMasses=len(atom_props)/3,
SampleDensity=self.getProperty("SampleDensity").value,
AtomicProperties=atom_props,
BeamRadius=self.getProperty("BeamRadius").value,
NumEventsPerRun=self.getProperty("NumEvents").value,
TotalScatteringWS=total_scatter_correction,
MultipleScatteringWS=multi_scatter_correction)
# Smooth the output
smooth_neighbours = self.getProperty("SmoothNeighbours").value
SmoothData(InputWorkspace=total_scatter_correction,
OutputWorkspace=total_scatter_correction,
NPoints=smooth_neighbours)
SmoothData(InputWorkspace=multi_scatter_correction,
OutputWorkspace=multi_scatter_correction,
NPoints=smooth_neighbours)
return total_scatter_correction, multi_scatter_correction
def PyExec(self):
from mantid.simpleapi import (ExtractSingleSpectrum, GroupWorkspaces,
Scale, Minus, DeleteWorkspace)
self._input_ws = self.getPropertyValue("InputWorkspace")
container_ws = self.getPropertyValue("ContainerWorkspace")
spec_idx = self.getProperty("WorkspaceIndex").value
self._output_ws = self.getPropertyValue("OutputWorkspace")
self._correction_wsg = self.getPropertyValue("CorrectionWorkspaces")
self._corrected_wsg = self.getPropertyValue("CorrectedWorkspaces")
self._linear_fit_table = self.getPropertyValue("LinearFitResult")
ExtractSingleSpectrum(InputWorkspace=self._input_ws,
OutputWorkspace=self._output_ws,
WorkspaceIndex=spec_idx)
self._correction_workspaces = list()
self._container_ws = None
if container_ws != "":
container_name = str(self._correction_wsg) + "_Container"
self._container_ws = ExtractSingleSpectrum(InputWorkspace=container_ws,
OutputWorkspace=container_name,
WorkspaceIndex=spec_idx)
self._correction_workspaces.append(self._container_ws.name())
# Do gamma correction
if self.getProperty("GammaBackground").value:
self._correction_workspaces.append(self._gamma_correction())
# Do multiple scattering correction
if self.getProperty("MultipleScattering").value:
self._correction_workspaces.extend(self._ms_correction())
# Output correction workspaces as a WorkspaceGroup
if self._correction_wsg != "":
GroupWorkspaces(InputWorkspaces=self._correction_workspaces,
OutputWorkspace=self._correction_wsg)
self.setProperty("CorrectionWorkspaces", self._correction_wsg)
# Perform fitting to obtain scale factors for corrections
# The workspaces to fit for correction scale factors
fit_corrections = [wks for wks in self._correction_workspaces if 'MultipleScattering' not in wks]
# Perform fitting of corrections
fixed_params = {}
fixed_gamma_factor = self.getProperty("GammaBackgroundScale").value
if fixed_gamma_factor != 0.0:
fixed_params['GammaBackground'] = fixed_gamma_factor
fixed_container_scale = self.getProperty("ContainerScale").value
if fixed_container_scale != 0.0:
fixed_params['Container'] = fixed_container_scale
params_ws = self._fit_corrections(fit_corrections, self._linear_fit_table, **fixed_params)
self.setProperty("LinearFitResult", params_ws)
# Scale gamma background
if self.getProperty("GammaBackground").value:
gamma_correct_ws = self._get_correction_workspace('GammaBackground')[1]
gamma_factor = self._get_correction_scale_factor('GammaBackground', fit_corrections, params_ws)
Scale(InputWorkspace=gamma_correct_ws,
OutputWorkspace=gamma_correct_ws,
Factor=gamma_factor)
# Scale multiple scattering
if self.getProperty("MultipleScattering").value:
# Use factor of total scattering as this includes single and multiple scattering
multi_scatter_correct_ws = self._get_correction_workspace('MultipleScattering')[1]
total_scatter_correct_ws = self._get_correction_workspace('TotalScattering')[1]
total_scatter_factor = self._get_correction_scale_factor('TotalScattering', fit_corrections, params_ws)
Scale(InputWorkspace=multi_scatter_correct_ws,
OutputWorkspace=multi_scatter_correct_ws,
Factor=total_scatter_factor)
Scale(InputWorkspace=total_scatter_correct_ws,
OutputWorkspace=total_scatter_correct_ws,
Factor=total_scatter_factor)
# Scale by container
if container_ws != "":
container_correct_ws = self._get_correction_workspace('Container')[1]
container_factor = self._get_correction_scale_factor('Container', fit_corrections, params_ws)
Scale(InputWorkspace=container_correct_ws,
OutputWorkspace=container_correct_ws,
Factor=container_factor)
# Calculate and output corrected workspaces as a WorkspaceGroup
if self._corrected_wsg != "":
corrected_workspaces = [ws_name.replace(self._correction_wsg, self._corrected_wsg) for ws_name in self._correction_workspaces]
for corrected, correction in zip(corrected_workspaces, self._correction_workspaces):
Minus(LHSWorkspace=self._output_ws,
RHSWorkspace=correction,
OutputWorkspace=corrected)
GroupWorkspaces(InputWorkspaces=corrected_workspaces,
OutputWorkspace=self._corrected_wsg)
self.setProperty("CorrectedWorkspaces", self._corrected_wsg)
# Apply corrections
for correction in self. _correction_workspaces:
if 'TotalScattering' not in correction:
Minus(LHSWorkspace=self._output_ws,
RHSWorkspace=correction,
OutputWorkspace=self._output_ws)
self.setProperty("OutputWorkspace", self._output_ws)
# Remove correction workspaces if they are no longer required
if self._correction_wsg == "":
for wksp in self._correction_workspaces:
DeleteWorkspace(wksp)
