def performOperation(self):
lhs_valid, rhs_valid, err_msg = self.validateInputs()
if err_msg != str():
return lhs_valid, rhs_valid, err_msg
lhs_ws, rhs_ws = self._scale_input_workspaces()
try:
if self._operation == '+':
if self._md_lhs or self._md_rhs:
PlusMD(LHSWorkspace=lhs_ws,
RHSWorkspace=rhs_ws,
OutputWorkspace=self._output_ws)
else:
Plus(LHSWorkspace=lhs_ws,
RHSWorkspace=rhs_ws,
OutputWorkspace=self._output_ws)
elif self._operation == '-':
if self._md_lhs or self._md_rhs:
MinusMD(LHSWorkspace=lhs_ws,
RHSWorkspace=rhs_ws,
OutputWorkspace=self._output_ws)
else:
Minus(LHSWorkspace=lhs_ws,
RHSWorkspace=rhs_ws,
OutputWorkspace=self._output_ws)
elif self._operation == '*':
if self._md_lhs or self._md_rhs:
MultiplyMD(LHSWorkspace=lhs_ws,
RHSWorkspace=rhs_ws,
OutputWorkspace=self._output_ws)
else:
Multiply(LHSWorkspace=lhs_ws,
RHSWorkspace=rhs_ws,
OutputWorkspace=self._output_ws)
elif self._operation == 'WM':
if self._md_lhs or self._md_rhs:
WeightedMeanMD(LHSWorkspace=lhs_ws,
RHSWorkspace=rhs_ws,
OutputWorkspace=self._output_ws)
else:
WeightedMean(InputWorkspace1=lhs_ws,
InputWorkspace2=rhs_ws,
OutputWorkspace=self._output_ws)
else:
if self._md_lhs or self._md_rhs:
DivideMD(LHSWorkspace=lhs_ws,
RHSWorkspace=rhs_ws,
OutputWorkspace=self._output_ws)
else:
Divide(LHSWorkspace=lhs_ws,
RHSWorkspace=rhs_ws,
OutputWorkspace=self._output_ws)
except (RuntimeError, ValueError) as err:
return False, False, str(err)
else:
self._regularize_output_names(self._output_ws)
finally:
DeleteWorkspaces(WorkspaceList=[lhs_ws, rhs_ws])
return True, True, ""
def _two_factor_corrections_approximation(self, sample_workspace,
container_workspace,
factor_workspaces):
acc = factor_workspaces['acc']
ass = factor_workspaces['ass']
minuend = Divide(sample_workspace, ass, StoreInADS=False)
subtrahend = Divide(container_workspace, acc, StoreInADS=False)
difference = Minus(minuend, subtrahend, OutputWorkspace="__difference")
return difference
def estimate_background(self, ws_name, niter, xwindow, doSGfilter):
try:
ws_bg = EnggEstimateFocussedBackground(InputWorkspace=ws_name, OutputWorkspace=ws_name + "_bg",
NIterations=niter, XWindow=xwindow, ApplyFilterSG=doSGfilter)
except (ValueError, RuntimeError) as e:
# ValueError when Niter not positive integer, RuntimeError when Window too small
logger.error("Error on arguments supplied to EnggEstimateFocusedBackground: " + str(e))
ws_bg = SetUncertainties(InputWorkspace=ws_name) # copy data and zero errors
ws_bg = Minus(LHSWorkspace=ws_bg, RHSWorkspace=ws_bg) # workspace of zeros with same num spectra
return ws_bg
def correctSampleData(self, sampleWsName, useVana, vanaWsName, useEmpty,
emptyWsName):
if useEmpty:
Minus(LHSWorkspace=sampleWsName,
RHSWorkspace=emptyWsName,
OutputWorkspace=sampleWsName)
if useVana:
Divide(LHSWorkspace=sampleWsName,
RHSWorkspace=vanaWsName,
OutputWorkspace=sampleWsName)
def plot_background_figure(self, ws_name):
ws = self._loaded_workspaces[ws_name]
ws_bgsub = self._bg_sub_workspaces[ws_name]
if ws_bgsub:
fig, ax = subplots(2, 1, sharex=True, gridspec_kw={'height_ratios': [2, 1]},
subplot_kw={'projection': 'mantid'})
bg = Minus(LHSWorkspace=ws_name, RHSWorkspace=ws_bgsub, StoreInADS=False)
ax[0].plot(ws, 'x')
ax[1].plot(ws_bgsub, 'x')
ax[0].plot(bg, '-r')
fig.show()
def _three_factor_corrections_approximation(self, sample_workspace,
container_workspace,
factor_workspaces):
acc = factor_workspaces['acc']
acsc = factor_workspaces['acsc']
assc = factor_workspaces['assc']
subtrahend = Multiply(container_workspace, (acsc / acc),
StoreInADS=False)
difference = Minus(sample_workspace, subtrahend, StoreInADS=False)
quotient = Divide(difference, assc, OutputWorkspace="__quotient")
return quotient
def create_or_update_bgsub_ws(self, ws_name, bg_params):
ws = self._loaded_workspaces[ws_name]
ws_bg = self._bg_sub_workspaces[ws_name]
if not ws_bg or self._bg_params[ws_name] == [] or bg_params[1:] != self._bg_params[ws_name][1:]:
background = self.estimate_background(ws_name, *bg_params[1:])
self._bg_params[ws_name] = bg_params
bgsub_ws_name = ws_name + "_bgsub"
bgsub_ws = Minus(LHSWorkspace=ws, RHSWorkspace=background, OutputWorkspace=bgsub_ws_name)
self._bg_sub_workspaces[ws_name] = bgsub_ws
DeleteWorkspace(background)
else:
logger.notice("Background workspace already calculated")
def _subtractEC(ws, ecWS, ecScaling, wsNames, wsCleanup, algorithmLogging):
"""Subtract empty container."""
# out = in - ecScaling * EC
scaledECWSName = wsNames.withSuffix('scaled_EC')
scaledECWS = Scale(InputWorkspace=ecWS,
Factor=ecScaling,
OutputWorkspace=scaledECWSName,
EnableLogging=algorithmLogging)
ecSubtractedWSName = wsNames.withSuffix('EC_subtracted')
ecSubtractedWS = Minus(LHSWorkspace=ws,
RHSWorkspace=scaledECWS,
OutputWorkspace=ecSubtractedWSName,
EnableLogging=algorithmLogging)
wsCleanup.cleanup(scaledECWS)
return ecSubtractedWS
def do_background_subtraction(self, ws_name, bg_params):
ws = self._loaded_workspaces[ws_name]
ws_bg = self._background_workspaces[ws_name]
bg_changed = False
if ws_bg and bg_params[1:] != self._bg_params[ws_name][1:]:
# add bg back on to data (but don't change bgsub status)
self.undo_background_subtraction(ws_name, isBGsub=bg_params[0])
bg_changed = True
if bg_changed or not ws_bg:
# re-evaluate background (or evaluate for first time)
self._bg_params[ws_name] = bg_params
ws_bg = self.estimate_background(ws_name, *bg_params[1:])
# update bg sub status before Minus (updates plot which repopulates table)
self._bg_params[ws_name][0] = bg_params[0]
Minus(LHSWorkspace=ws, RHSWorkspace=ws_bg, OutputWorkspace=ws_name)
def _subtractFlatBkg(self, ws):
"""Return a workspace where a flat background has been subtracted from ws."""
method = self.getProperty(Prop.BKG_METHOD).value
if method == BkgMethod.OFF:
return ws
clonedWSName = self._names.withSuffix('cloned_for_flat_bkg')
clonedWS = CloneWorkspace(
InputWorkspace=ws,
OutputWorkspace=clonedWSName,
EnableLogging=self._subalgLogging
)
transposedWSName = self._names.withSuffix('transposed_clone')
transposedWS = Transpose(
InputWorkspace=clonedWS,
OutputWorkspace=transposedWSName,
EnableLogging=self._subalgLogging
)
self._cleanup.cleanup(clonedWS)
ranges = self._flatBkgRanges(ws)
polynomialDegree = 0 if self.getProperty(Prop.BKG_METHOD).value == BkgMethod.CONSTANT else 1
transposedBkgWSName = self._names.withSuffix('transposed_flat_background')
transposedBkgWS = CalculatePolynomialBackground(
InputWorkspace=transposedWS,
OutputWorkspace=transposedBkgWSName,
Degree=polynomialDegree,
XRanges=ranges,
CostFunction='Unweighted least squares',
EnableLogging=self._subalgLogging
)
self._cleanup.cleanup(transposedWS)
bkgWSName = self._names.withSuffix('flat_background')
bkgWS = Transpose(
InputWorkspace=transposedBkgWS,
OutputWorkspace=bkgWSName,
EnableLogging=self._subalgLogging
)
self._cleanup.cleanup(transposedBkgWS)
subtractedWSName = self._names.withSuffix('flat_background_subtracted')
subtractedWS = Minus(
LHSWorkspace=ws,
RHSWorkspace=bkgWS,
OutputWorkspace=subtractedWSName,
EnableLogging=self._subalgLogging
)
self._cleanup.cleanup(ws)
self._cleanup.cleanup(bkgWS)
return subtractedWS
def plot_background_figure(self, ws_name):
def on_draw(event):
if event.canvas.signalsBlocked():
# This stops infinite loop as draw() is called within this handle (and set signalsBlocked == True)
# Resets signalsBlocked to False (default value)
event.canvas.blockSignals(False)
else:
axes = event.canvas.figure.get_axes()
data_line = next(
(line for line in axes[0].get_tracked_artists()), None)
bg_line = next((line for line in axes[0].get_lines()
if line not in axes[0].get_tracked_artists()),
None)
bgsub_line = next(
(line for line in axes[1].get_tracked_artists()), None)
if data_line and bg_line and bgsub_line:
event.canvas.blockSignals(
True
) # this doesn't stop this handle being called again on canvas.draw()
bg_line.set_ydata(data_line.get_ydata() -
bgsub_line.get_ydata())
event.canvas.draw()
else:
# would like to close the fig at this point but this interferes with the mantid ADS observers when
# any of the tracked workspaces are deleted and causes mantid to hard crash - so just print warning
logger.warning(
f"Inspect background figure {event.canvas.figure.number} has been invalidated - the "
f"background curve will no longer be updated.")
ws = self._data_workspaces[ws_name].loaded_ws
ws_bgsub = self._data_workspaces[ws_name].bgsub_ws
if ws_bgsub:
fig, ax = subplots(2,
1,
sharex=True,
gridspec_kw={'height_ratios': [2, 1]},
subplot_kw={'projection': 'mantid'})
bg = Minus(LHSWorkspace=ws_name,
RHSWorkspace=ws_bgsub,
StoreInADS=False)
ax[0].plot(ws, 'x')
ax[1].plot(ws_bgsub, 'x', label='background subtracted data')
ax[0].plot(bg, '-r', label='background')
ax[0].legend(fontsize=8.0)
ax[1].legend(fontsize=8.0)
fig.canvas.mpl_connect("draw_event", on_draw)
fig.show()
def _subtractFlatBkg(ws, wsType, bkgWorkspace, bkgScaling, wsNames, wsCleanup, algorithmLogging):
"""Subtract a scaled flat background from a workspace."""
if wsType == common.WS_CONTENT_DETS:
subtractedWSName = wsNames.withSuffix('flat_bkg_subtracted_detectors')
scaledBkgWSName = wsNames.withSuffix('flat_bkg_for_detectors_scaled')
else:
subtractedWSName = wsNames.withSuffix('flat_bkg_subtracted_monitors')
scaledBkgWSName = wsNames.withSuffix('flat_bkg_for_monitors_scaled')
Scale(InputWorkspace=bkgWorkspace,
OutputWorkspace=scaledBkgWSName,
Factor=bkgScaling,
EnableLogging=algorithmLogging)
subtractedWS = Minus(LHSWorkspace=ws,
RHSWorkspace=scaledBkgWSName,
OutputWorkspace=subtractedWSName,
EnableLogging=algorithmLogging)
wsCleanup.cleanup(scaledBkgWSName)
return subtractedWS
def _subtractEC(ws, ecWS, ecScaling, wsNames, wsCleanup, algorithmLogging):
"""Subtract empty container."""
# out = in - ecScaling * EC
scalingWSName = wsNames.withSuffix('ecScaling')
scalingWS = CreateSingleValuedWorkspace(OutputWorkspace=scalingWSName,
DataValue=ecScaling,
EnableLogging=algorithmLogging)
scaledECWSName = wsNames.withSuffix('scaled_EC')
scaledECWS = Multiply(LHSWorkspace=ecWS,
RHSWorkspace=scalingWS,
OutputWorkspace=scaledECWSName,
EnableLogging=algorithmLogging)
ecSubtractedWSName = wsNames.withSuffix('EC_subtracted')
ecSubtractedWS = Minus(LHSWorkspace=ws,
RHSWorkspace=scaledECWS,
OutputWorkspace=ecSubtractedWSName,
EnableLogging=algorithmLogging)
wsCleanup.cleanup(scalingWS)
wsCleanup.cleanup(scaledECWS)
return ecSubtractedWS
def TotalScatteringReduction(config=None):
facility = config['Facility']
title = config['Title']
instr = config['Instrument']
# Get an instance to Mantid's logger
log = Logger("TotalScatteringReduction")
# Get sample info
sample = get_sample(config)
sam_mass_density = sample.get('MassDensity', None)
sam_packing_fraction = sample.get('PackingFraction', None)
sam_geometry = sample.get('Geometry', None)
sam_material = sample.get('Material', None)
sam_geo_dict = {
'Shape': 'Cylinder',
'Radius': config['Sample']['Geometry']['Radius'],
'Height': config['Sample']['Geometry']['Height']
}
sam_mat_dict = {
'ChemicalFormula': sam_material,
'SampleMassDensity': sam_mass_density
}
if 'Environment' in config:
sam_env_dict = {
'Name': config['Environment']['Name'],
'Container': config['Environment']['Container']
}
else:
sam_env_dict = {'Name': 'InAir', 'Container': 'PAC06'}
# Get normalization info
van = get_normalization(config)
van_mass_density = van.get('MassDensity', None)
van_packing_fraction = van.get('PackingFraction', 1.0)
van_geometry = van.get('Geometry', None)
van_material = van.get('Material', 'V')
van_geo_dict = {
'Shape': 'Cylinder',
'Radius': config['Normalization']['Geometry']['Radius'],
'Height': config['Normalization']['Geometry']['Height']
}
van_mat_dict = {
'ChemicalFormula': van_material,
'SampleMassDensity': van_mass_density
}
# Get calibration, characterization, and other settings
merging = config['Merging']
binning = merging['QBinning']
characterizations = merging.get('Characterizations', None)
# Grouping
grouping = merging.get('Grouping', None)
cache_dir = config.get("CacheDir", os.path.abspath('.'))
OutputDir = config.get("OutputDir", os.path.abspath('.'))
# Create Nexus file basenames
sample['Runs'] = expand_ints(sample['Runs'])
sample['Background']['Runs'] = expand_ints(sample['Background'].get(
'Runs', None))
'''
Currently not implemented:
# wkspIndices = merging.get('SumBanks', None)
# high_q_linear_fit_range = config['HighQLinearFitRange']
POWGEN options not used
#alignAndFocusArgs['RemovePromptPulseWidth'] = 50
# alignAndFocusArgs['CompressTolerance'] use defaults
# alignAndFocusArgs['UnwrapRef'] POWGEN option
# alignAndFocusArgs['LowResRef'] POWGEN option
# alignAndFocusArgs['LowResSpectrumOffset'] POWGEN option
How much of each bank gets merged has info here in the form of
# {"ID", "Qmin", "QMax"}
# alignAndFocusArgs['CropWavelengthMin'] from characterizations file
# alignAndFocusArgs['CropWavelengthMax'] from characterizations file
'''
if facility == 'SNS':
facility_file_format = '%s_%d'
else:
facility_file_format = '%s%d'
sam_scans = ','.join(
[facility_file_format % (instr, num) for num in sample['Runs']])
container_scans = ','.join([
facility_file_format % (instr, num)
for num in sample['Background']["Runs"]
])
container_bg = None
if "Background" in sample['Background']:
sample['Background']['Background']['Runs'] = expand_ints(
sample['Background']['Background']['Runs'])
container_bg = ','.join([
facility_file_format % (instr, num)
for num in sample['Background']['Background']['Runs']
])
if len(container_bg) == 0:
container_bg = None
van['Runs'] = expand_ints(van['Runs'])
van_scans = ','.join(
[facility_file_format % (instr, num) for num in van['Runs']])
van_bg_scans = None
if 'Background' in van:
van_bg_scans = van['Background']['Runs']
van_bg_scans = expand_ints(van_bg_scans)
van_bg_scans = ','.join(
[facility_file_format % (instr, num) for num in van_bg_scans])
# Override Nexus file basename with Filenames if present
if "Filenames" in sample:
sam_scans = ','.join(sample["Filenames"])
if "Filenames" in sample['Background']:
container_scans = ','.join(sample['Background']["Filenames"])
if "Background" in sample['Background']:
if "Filenames" in sample['Background']['Background']:
container_bg = ','.join(
sample['Background']['Background']['Filenames'])
if "Filenames" in van:
van_scans = ','.join(van["Filenames"])
if "Background" in van:
if "Filenames" in van['Background']:
van_bg_scans = ','.join(van['Background']["Filenames"])
# Output nexus filename
nexus_filename = title + '.nxs'
try:
os.remove(nexus_filename)
except OSError:
pass
# Get sample corrections
sam_abs_corr = sample.get("AbsorptionCorrection", None)
sam_ms_corr = sample.get("MultipleScatteringCorrection", None)
sam_inelastic_corr = SetInelasticCorrection(
sample.get('InelasticCorrection', None))
# Warn about having absorption correction and multiple scat correction set
if sam_abs_corr and sam_ms_corr:
log.warning(MS_AND_ABS_CORR_WARNING)
# Compute the absorption correction on the sample if it was provided
sam_abs_ws = ''
con_abs_ws = ''
if sam_abs_corr:
msg = "Applying '{}' absorption correction to sample"
log.notice(msg.format(sam_abs_corr["Type"]))
sam_abs_ws, con_abs_ws = create_absorption_wksp(
sam_scans, sam_abs_corr["Type"], sam_geo_dict, sam_mat_dict,
sam_env_dict, **config)
# Get vanadium corrections
van_mass_density = van.get('MassDensity', van_mass_density)
van_packing_fraction = van.get('PackingFraction', van_packing_fraction)
van_abs_corr = van.get("AbsorptionCorrection", {"Type": None})
van_ms_corr = van.get("MultipleScatteringCorrection", {"Type": None})
van_inelastic_corr = SetInelasticCorrection(
van.get('InelasticCorrection', None))
# Warn about having absorption correction and multiple scat correction set
if van_abs_corr["Type"] and van_ms_corr["Type"]:
log.warning(MS_AND_ABS_CORR_WARNING)
# Compute the absorption correction for the vanadium if provided
van_abs_corr_ws = ''
if van_abs_corr:
msg = "Applying '{}' absorption correction to vanadium"
log.notice(msg.format(van_abs_corr["Type"]))
van_abs_corr_ws, van_con_ws = create_absorption_wksp(
van_scans, van_abs_corr["Type"], van_geo_dict, van_mat_dict,
**config)
alignAndFocusArgs = dict()
alignAndFocusArgs['CalFilename'] = config['Calibration']['Filename']
# alignAndFocusArgs['GroupFilename'] don't use
# alignAndFocusArgs['Params'] = "0.,0.02,40."
alignAndFocusArgs['ResampleX'] = -6000
alignAndFocusArgs['Dspacing'] = False
alignAndFocusArgs['PreserveEvents'] = False
alignAndFocusArgs['MaxChunkSize'] = 8
alignAndFocusArgs['CacheDir'] = os.path.abspath(cache_dir)
# Get any additional AlignAndFocusArgs from JSON input
if "AlignAndFocusArgs" in config:
otherArgs = config["AlignAndFocusArgs"]
alignAndFocusArgs.update(otherArgs)
# Setup grouping
output_grouping = False
grp_wksp = "wksp_output_group"
if grouping:
if 'Initial' in grouping:
if grouping['Initial'] and not grouping['Initial'] == u'':
alignAndFocusArgs['GroupFilename'] = grouping['Initial']
if 'Output' in grouping:
if grouping['Output'] and not grouping['Output'] == u'':
output_grouping = True
LoadDetectorsGroupingFile(InputFile=grouping['Output'],
OutputWorkspace=grp_wksp)
# If no output grouping specified, create it with Calibration Grouping
if not output_grouping:
LoadDiffCal(alignAndFocusArgs['CalFilename'],
InstrumentName=instr,
WorkspaceName=grp_wksp.replace('_group', ''),
MakeGroupingWorkspace=True,
MakeCalWorkspace=False,
MakeMaskWorkspace=False)
# Setup the 6 bank method if no grouping specified
if not grouping:
CreateGroupingWorkspace(InstrumentName=instr,
GroupDetectorsBy='Group',
OutputWorkspace=grp_wksp)
alignAndFocusArgs['GroupingWorkspace'] = grp_wksp
# TODO take out the RecalculatePCharge in the future once tested
# Load Sample
print("#-----------------------------------#")
print("# Sample")
print("#-----------------------------------#")
sam_wksp = load('sample', sam_scans, sam_geometry, sam_material,
sam_mass_density, sam_abs_ws, **alignAndFocusArgs)
sample_title = "sample_and_container"
save_banks(InputWorkspace=sam_wksp,
Filename=nexus_filename,
Title=sample_title,
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
sam_molecular_mass = mtd[sam_wksp].sample().getMaterial(
).relativeMolecularMass()
natoms = getNumberAtoms(sam_packing_fraction,
sam_mass_density,
sam_molecular_mass,
Geometry=sam_geometry)
# Load Sample Container
print("#-----------------------------------#")
print("# Sample Container")
print("#-----------------------------------#")
container = load('container',
container_scans,
absorption_wksp=con_abs_ws,
**alignAndFocusArgs)
save_banks(InputWorkspace=container,
Filename=nexus_filename,
Title=container,
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
# Load Sample Container Background
if container_bg is not None:
print("#-----------------------------------#")
print("# Sample Container's Background")
print("#-----------------------------------#")
container_bg = load('container_background', container_bg,
**alignAndFocusArgs)
save_banks(InputWorkspace=container_bg,
Filename=nexus_filename,
Title=container_bg,
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
# Load Vanadium
print("#-----------------------------------#")
print("# Vanadium")
print("#-----------------------------------#")
van_wksp = load('vanadium', van_scans, van_geometry, van_material,
van_mass_density, van_abs_corr_ws, **alignAndFocusArgs)
vanadium_title = "vanadium_and_background"
save_banks(InputWorkspace=van_wksp,
Filename=nexus_filename,
Title=vanadium_title,
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
van_material = mtd[van_wksp].sample().getMaterial()
van_molecular_mass = van_material.relativeMolecularMass()
nvan_atoms = getNumberAtoms(1.0,
van_mass_density,
van_molecular_mass,
Geometry=van_geometry)
print("Sample natoms:", natoms)
print("Vanadium natoms:", nvan_atoms)
print("Vanadium natoms / Sample natoms:", nvan_atoms / natoms)
# Load Vanadium Background
van_bg = None
if van_bg_scans is not None:
print("#-----------------------------------#")
print("# Vanadium Background")
print("#-----------------------------------#")
van_bg = load('vanadium_background', van_bg_scans, **alignAndFocusArgs)
vanadium_bg_title = "vanadium_background"
save_banks(InputWorkspace=van_bg,
Filename=nexus_filename,
Title=vanadium_bg_title,
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
# Load Instrument Characterizations
if characterizations:
PDDetermineCharacterizations(
InputWorkspace=sam_wksp,
Characterizations='characterizations',
ReductionProperties='__snspowderreduction')
propMan = PropertyManagerDataService.retrieve('__snspowderreduction')
qmax = 2. * np.pi / propMan['d_min'].value
qmin = 2. * np.pi / propMan['d_max'].value
for a, b in zip(qmin, qmax):
print('Qrange:', a, b)
# TODO: Add when we apply Qmin, Qmax cropping
# mask_info = generate_cropping_table(qmin, qmax)
# STEP 1: Subtract Backgrounds
sam_raw = 'sam_raw'
CloneWorkspace(InputWorkspace=sam_wksp,
OutputWorkspace=sam_raw) # for later
container_raw = 'container_raw'
CloneWorkspace(InputWorkspace=container,
OutputWorkspace=container_raw) # for later
if van_bg is not None:
RebinToWorkspace(WorkspaceToRebin=van_bg,
WorkspaceToMatch=van_wksp,
OutputWorkspace=van_bg)
Minus(LHSWorkspace=van_wksp,
RHSWorkspace=van_bg,
OutputWorkspace=van_wksp)
RebinToWorkspace(WorkspaceToRebin=container,
WorkspaceToMatch=sam_wksp,
OutputWorkspace=container)
Minus(LHSWorkspace=sam_wksp,
RHSWorkspace=container,
OutputWorkspace=sam_wksp)
if container_bg is not None:
RebinToWorkspace(WorkspaceToRebin=container_bg,
WorkspaceToMatch=container,
OutputWorkspace=container_bg)
Minus(LHSWorkspace=container,
RHSWorkspace=container_bg,
OutputWorkspace=container)
for wksp in [container, van_wksp, sam_wksp]:
ConvertUnits(InputWorkspace=wksp,
OutputWorkspace=wksp,
Target="MomentumTransfer",
EMode="Elastic")
container_title = "container_minus_back"
vanadium_title = "vanadium_minus_back"
sample_title = "sample_minus_back"
save_banks(InputWorkspace=container,
Filename=nexus_filename,
Title=container_title,
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
save_banks(InputWorkspace=van_wksp,
Filename=nexus_filename,
Title=vanadium_title,
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
save_banks(InputWorkspace=sam_wksp,
Filename=nexus_filename,
Title=sample_title,
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
# STEP 2.0: Prepare vanadium as normalization calibrant
# Multiple-Scattering and Absorption (Steps 2-4) for Vanadium
van_corrected = 'van_corrected'
ConvertUnits(InputWorkspace=van_wksp,
OutputWorkspace=van_corrected,
Target="Wavelength",
EMode="Elastic")
if "Type" in van_abs_corr:
if van_abs_corr['Type'] == 'Carpenter' \
or van_ms_corr['Type'] == 'Carpenter':
CarpenterSampleCorrection(
InputWorkspace=van_corrected,
OutputWorkspace=van_corrected,
CylinderSampleRadius=van['Geometry']['Radius'])
elif van_abs_corr['Type'] == 'Mayers' \
or van_ms_corr['Type'] == 'Mayers':
if van_ms_corr['Type'] == 'Mayers':
MayersSampleCorrection(InputWorkspace=van_corrected,
OutputWorkspace=van_corrected,
MultipleScattering=True)
else:
MayersSampleCorrection(InputWorkspace=van_corrected,
OutputWorkspace=van_corrected,
MultipleScattering=False)
else:
print("NO VANADIUM absorption or multiple scattering!")
else:
CloneWorkspace(InputWorkspace=van_corrected,
OutputWorkspace=van_corrected)
ConvertUnits(InputWorkspace=van_corrected,
OutputWorkspace=van_corrected,
Target='MomentumTransfer',
EMode='Elastic')
vanadium_title += "_ms_abs_corrected"
save_banks(InputWorkspace=van_corrected,
Filename=nexus_filename,
Title=vanadium_title,
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
save_banks(InputWorkspace=van_corrected,
Filename=nexus_filename,
Title=vanadium_title + "_with_peaks",
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
# TODO subtract self-scattering of vanadium (According to Eq. 7 of Howe,
# McGreevey, and Howells, JPCM, 1989)
# Smooth Vanadium (strip peaks plus smooth)
ConvertUnits(InputWorkspace=van_corrected,
OutputWorkspace=van_corrected,
Target='dSpacing',
EMode='Elastic')
# After StripVanadiumPeaks, the workspace goes from EventWorkspace ->
# Workspace2D
StripVanadiumPeaks(InputWorkspace=van_corrected,
OutputWorkspace=van_corrected,
BackgroundType='Quadratic')
ConvertUnits(InputWorkspace=van_corrected,
OutputWorkspace=van_corrected,
Target='MomentumTransfer',
EMode='Elastic')
vanadium_title += '_peaks_stripped'
save_banks(InputWorkspace=van_corrected,
Filename=nexus_filename,
Title=vanadium_title,
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
ConvertUnits(InputWorkspace=van_corrected,
OutputWorkspace=van_corrected,
Target='TOF',
EMode='Elastic')
FFTSmooth(InputWorkspace=van_corrected,
OutputWorkspace=van_corrected,
Filter="Butterworth",
Params='20,2',
IgnoreXBins=True,
AllSpectra=True)
ConvertUnits(InputWorkspace=van_corrected,
OutputWorkspace=van_corrected,
Target='MomentumTransfer',
EMode='Elastic')
vanadium_title += '_smoothed'
save_banks(InputWorkspace=van_corrected,
Filename=nexus_filename,
Title=vanadium_title,
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
# Inelastic correction
if van_inelastic_corr['Type'] == "Placzek":
van_scan = van['Runs'][0]
van_incident_wksp = 'van_incident_wksp'
van_inelastic_opts = van['InelasticCorrection']
lambda_binning_fit = van_inelastic_opts['LambdaBinningForFit']
lambda_binning_calc = van_inelastic_opts['LambdaBinningForCalc']
print('van_scan:', van_scan)
GetIncidentSpectrumFromMonitor(Filename=facility_file_format %
(instr, van_scan),
OutputWorkspace=van_incident_wksp)
fit_type = van['InelasticCorrection']['FitSpectrumWith']
FitIncidentSpectrum(InputWorkspace=van_incident_wksp,
OutputWorkspace=van_incident_wksp,
FitSpectrumWith=fit_type,
BinningForFit=lambda_binning_fit,
BinningForCalc=lambda_binning_calc,
PlotDiagnostics=False)
van_placzek = 'van_placzek'
SetSample(InputWorkspace=van_incident_wksp,
Material={
'ChemicalFormula': str(van_material),
'SampleMassDensity': str(van_mass_density)
})
CalculatePlaczekSelfScattering(IncidentWorkspace=van_incident_wksp,
ParentWorkspace=van_corrected,
OutputWorkspace=van_placzek,
L1=19.5,
L2=alignAndFocusArgs['L2'],
Polar=alignAndFocusArgs['Polar'])
ConvertToHistogram(InputWorkspace=van_placzek,
OutputWorkspace=van_placzek)
# Save before rebin in Q
for wksp in [van_placzek, van_corrected]:
ConvertUnits(InputWorkspace=wksp,
OutputWorkspace=wksp,
Target='MomentumTransfer',
EMode='Elastic')
Rebin(InputWorkspace=wksp,
OutputWorkspace=wksp,
Params=binning,
PreserveEvents=True)
save_banks(InputWorkspace=van_placzek,
Filename=nexus_filename,
Title="vanadium_placzek",
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
# Rebin in Wavelength
for wksp in [van_placzek, van_corrected]:
ConvertUnits(InputWorkspace=wksp,
OutputWorkspace=wksp,
Target='Wavelength',
EMode='Elastic')
Rebin(InputWorkspace=wksp,
OutputWorkspace=wksp,
Params=lambda_binning_calc,
PreserveEvents=True)
# Save after rebin in Q
for wksp in [van_placzek, van_corrected]:
ConvertUnits(InputWorkspace=wksp,
OutputWorkspace=wksp,
Target='MomentumTransfer',
EMode='Elastic')
# Subtract correction in Wavelength
for wksp in [van_placzek, van_corrected]:
ConvertUnits(InputWorkspace=wksp,
OutputWorkspace=wksp,
Target='Wavelength',
EMode='Elastic')
if not mtd[wksp].isDistribution():
ConvertToDistribution(wksp)
Minus(LHSWorkspace=van_corrected,
RHSWorkspace=van_placzek,
OutputWorkspace=van_corrected)
# Save after subtraction
for wksp in [van_placzek, van_corrected]:
ConvertUnits(InputWorkspace=wksp,
OutputWorkspace=wksp,
Target='MomentumTransfer',
EMode='Elastic')
vanadium_title += '_placzek_corrected'
save_banks(InputWorkspace=van_corrected,
Filename=nexus_filename,
Title=vanadium_title,
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
ConvertUnits(InputWorkspace=van_corrected,
OutputWorkspace=van_corrected,
Target='MomentumTransfer',
EMode='Elastic')
SetUncertainties(InputWorkspace=van_corrected,
OutputWorkspace=van_corrected,
SetError='zero')
# STEP 2.1: Normalize by Vanadium
wksp_list = [sam_wksp, sam_raw, van_corrected]
for name in wksp_list:
ConvertUnits(InputWorkspace=name,
OutputWorkspace=name,
Target='MomentumTransfer',
EMode='Elastic',
ConvertFromPointData=False)
Rebin(InputWorkspace=name,
OutputWorkspace=name,
Params=binning,
PreserveEvents=True)
# Save the sample - back / normalized
Divide(LHSWorkspace=sam_wksp,
RHSWorkspace=van_corrected,
OutputWorkspace=sam_wksp)
sample_title += "_normalized"
save_banks(InputWorkspace=sam_wksp,
Filename=nexus_filename,
Title=sample_title,
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
# Save the sample / normalized (ie no background subtraction)
Divide(LHSWorkspace=sam_raw,
RHSWorkspace=van_corrected,
OutputWorkspace=sam_raw)
save_banks(InputWorkspace=sam_raw,
Filename=nexus_filename,
Title="sample_normalized",
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
# Output an initial I(Q) for sample
iq_filename = title + '_initial_iofq_banks.nxs'
save_banks(InputWorkspace=sam_wksp,
Filename=iq_filename,
Title="IQ_banks",
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
wksp_list = [container, container_raw, van_corrected]
if container_bg is not None:
wksp_list.append(container_bg)
if van_bg is not None:
wksp_list.append(van_bg)
for name in wksp_list:
ConvertUnits(InputWorkspace=name,
OutputWorkspace=name,
Target='MomentumTransfer',
EMode='Elastic',
ConvertFromPointData=False)
Rebin(InputWorkspace=name,
OutputWorkspace=name,
Params=binning,
PreserveEvents=True)
# Save the container - container_background / normalized
Divide(LHSWorkspace=container,
RHSWorkspace=van_corrected,
OutputWorkspace=container)
container_title += '_normalized'
save_banks(InputWorkspace=container,
Filename=nexus_filename,
Title=container_title,
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
# Save the container / normalized (ie no background subtraction)
Divide(LHSWorkspace=container_raw,
RHSWorkspace=van_corrected,
OutputWorkspace=container_raw)
save_banks(InputWorkspace=container_raw,
Filename=nexus_filename,
Title="container_normalized",
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
# Save the container_background / normalized
if container_bg is not None:
Divide(LHSWorkspace=container_bg,
RHSWorkspace=van_corrected,
OutputWorkspace=container_bg)
container_bg_title = "container_back_normalized"
save_banks(InputWorkspace=container_bg,
Filename=nexus_filename,
Title=container_bg_title,
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
# Save the vanadium_background / normalized
if van_bg is not None:
Divide(LHSWorkspace=van_bg,
RHSWorkspace=van_corrected,
OutputWorkspace=van_bg)
vanadium_bg_title += "_normalized"
save_banks(InputWorkspace=van_bg,
Filename=nexus_filename,
Title=vanadium_bg_title,
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
# STEP 3 & 4: Subtract multiple scattering and apply absorption correction
ConvertUnits(InputWorkspace=sam_wksp,
OutputWorkspace=sam_wksp,
Target="Wavelength",
EMode="Elastic")
sam_corrected = 'sam_corrected'
if sam_abs_corr and sam_ms_corr:
if sam_abs_corr['Type'] == 'Carpenter' \
or sam_ms_corr['Type'] == 'Carpenter':
CarpenterSampleCorrection(
InputWorkspace=sam_wksp,
OutputWorkspace=sam_corrected,
CylinderSampleRadius=sample['Geometry']['Radius'])
elif sam_abs_corr['Type'] == 'Mayers' \
or sam_ms_corr['Type'] == 'Mayers':
if sam_ms_corr['Type'] == 'Mayers':
MayersSampleCorrection(InputWorkspace=sam_wksp,
OutputWorkspace=sam_corrected,
MultipleScattering=True)
else:
MayersSampleCorrection(InputWorkspace=sam_wksp,
OutputWorkspace=sam_corrected,
MultipleScattering=False)
else:
print("NO SAMPLE absorption or multiple scattering!")
CloneWorkspace(InputWorkspace=sam_wksp,
OutputWorkspace=sam_corrected)
ConvertUnits(InputWorkspace=sam_corrected,
OutputWorkspace=sam_corrected,
Target='MomentumTransfer',
EMode='Elastic')
sample_title += "_ms_abs_corrected"
save_banks(InputWorkspace=sam_corrected,
Filename=nexus_filename,
Title=sample_title,
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
else:
CloneWorkspace(InputWorkspace=sam_wksp, OutputWorkspace=sam_corrected)
# STEP 5: Divide by number of atoms in sample
mtd[sam_corrected] = (nvan_atoms / natoms) * mtd[sam_corrected]
ConvertUnits(InputWorkspace=sam_corrected,
OutputWorkspace=sam_corrected,
Target='MomentumTransfer',
EMode='Elastic')
sample_title += "_norm_by_atoms"
save_banks(InputWorkspace=sam_corrected,
Filename=nexus_filename,
Title=sample_title,
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
# STEP 6: Divide by total scattering length squared = total scattering
# cross-section over 4 * pi
van_material = mtd[van_corrected].sample().getMaterial()
sigma_v = van_material.totalScatterXSection()
prefactor = (sigma_v / (4. * np.pi))
msg = "Total scattering cross-section of Vanadium:{} sigma_v / 4*pi: {}"
print(msg.format(sigma_v, prefactor))
mtd[sam_corrected] = prefactor * mtd[sam_corrected]
sample_title += '_multiply_by_vanSelfScat'
save_banks(InputWorkspace=sam_corrected,
Filename=nexus_filename,
Title=sample_title,
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
# STEP 7: Inelastic correction
ConvertUnits(InputWorkspace=sam_corrected,
OutputWorkspace=sam_corrected,
Target='Wavelength',
EMode='Elastic')
if sam_inelastic_corr['Type'] == "Placzek":
if sam_material is None:
error = "For Placzek correction, must specifiy a sample material."
raise Exception(error)
for sam_scan in sample['Runs']:
sam_incident_wksp = 'sam_incident_wksp'
sam_inelastic_opts = sample['InelasticCorrection']
lambda_binning_fit = sam_inelastic_opts['LambdaBinningForFit']
lambda_binning_calc = sam_inelastic_opts['LambdaBinningForCalc']
GetIncidentSpectrumFromMonitor(Filename=facility_file_format %
(instr, sam_scan),
OutputWorkspace=sam_incident_wksp)
fit_type = sample['InelasticCorrection']['FitSpectrumWith']
FitIncidentSpectrum(InputWorkspace=sam_incident_wksp,
OutputWorkspace=sam_incident_wksp,
FitSpectrumWith=fit_type,
BinningForFit=lambda_binning_fit,
BinningForCalc=lambda_binning_calc)
sam_placzek = 'sam_placzek'
SetSample(InputWorkspace=sam_incident_wksp,
Material={
'ChemicalFormula': str(sam_material),
'SampleMassDensity': str(sam_mass_density)
})
CalculatePlaczekSelfScattering(IncidentWorkspace=sam_incident_wksp,
ParentWorkspace=sam_corrected,
OutputWorkspace=sam_placzek,
L1=19.5,
L2=alignAndFocusArgs['L2'],
Polar=alignAndFocusArgs['Polar'])
ConvertToHistogram(InputWorkspace=sam_placzek,
OutputWorkspace=sam_placzek)
# Save before rebin in Q
for wksp in [sam_placzek, sam_corrected]:
ConvertUnits(InputWorkspace=wksp,
OutputWorkspace=wksp,
Target='MomentumTransfer',
EMode='Elastic')
Rebin(InputWorkspace=wksp,
OutputWorkspace=wksp,
Params=binning,
PreserveEvents=True)
save_banks(InputWorkspace=sam_placzek,
Filename=nexus_filename,
Title="sample_placzek",
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
# Save after rebin in Q
for wksp in [sam_placzek, sam_corrected]:
ConvertUnits(InputWorkspace=wksp,
OutputWorkspace=wksp,
Target='MomentumTransfer',
EMode='Elastic')
Minus(LHSWorkspace=sam_corrected,
RHSWorkspace=sam_placzek,
OutputWorkspace=sam_corrected)
# Save after subtraction
for wksp in [sam_placzek, sam_corrected]:
ConvertUnits(InputWorkspace=wksp,
OutputWorkspace=wksp,
Target='MomentumTransfer',
EMode='Elastic')
sample_title += '_placzek_corrected'
save_banks(InputWorkspace=sam_corrected,
Filename=nexus_filename,
Title=sample_title,
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
# STEP 7: Output spectrum
# TODO Since we already went from Event -> 2D workspace, can't use this
# anymore
print('sam:', mtd[sam_corrected].id())
print('van:', mtd[van_corrected].id())
if alignAndFocusArgs['PreserveEvents']:
CompressEvents(InputWorkspace=sam_corrected,
OutputWorkspace=sam_corrected)
# F(Q) bank-by-bank Section
fq_banks_wksp = "FQ_banks_wksp"
CloneWorkspace(InputWorkspace=sam_corrected, OutputWorkspace=fq_banks_wksp)
# TODO: Add the following when implemented - FQ_banks = 'FQ_banks'
# S(Q) bank-by-bank Section
material = mtd[sam_corrected].sample().getMaterial()
if material.name() is None or len(material.name().strip()) == 0:
raise RuntimeError('Sample material was not set')
bcoh_avg_sqrd = material.cohScatterLength() * material.cohScatterLength()
btot_sqrd_avg = material.totalScatterLengthSqrd()
laue_monotonic_diffuse_scat = btot_sqrd_avg / bcoh_avg_sqrd
sq_banks_wksp = 'SQ_banks_wksp'
CloneWorkspace(InputWorkspace=sam_corrected, OutputWorkspace=sq_banks_wksp)
# TODO: Add the following when implemented
'''
SQ_banks = (1. / bcoh_avg_sqrd) * \
mtd[sq_banks_wksp] - laue_monotonic_diffuse_scat + 1.
'''
# Save S(Q) and F(Q) to diagnostics NeXus file
save_banks(InputWorkspace=fq_banks_wksp,
Filename=nexus_filename,
Title="FQ_banks",
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
save_banks(InputWorkspace=sq_banks_wksp,
Filename=nexus_filename,
Title="SQ_banks",
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
# Output a main S(Q) and F(Q) file
fq_filename = title + '_fofq_banks_corrected.nxs'
save_banks(InputWorkspace=fq_banks_wksp,
Filename=fq_filename,
Title="FQ_banks",
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
sq_filename = title + '_sofq_banks_corrected.nxs'
save_banks(InputWorkspace=sq_banks_wksp,
Filename=sq_filename,
Title="SQ_banks",
OutputDir=OutputDir,
GroupingWorkspace=grp_wksp,
Binning=binning)
# Print log information
print("<b>^2:", bcoh_avg_sqrd)
print("<b^2>:", btot_sqrd_avg)
print("Laue term:", laue_monotonic_diffuse_scat)
print("sample total xsection:",
mtd[sam_corrected].sample().getMaterial().totalScatterXSection())
print("vanadium total xsection:",
mtd[van_corrected].sample().getMaterial().totalScatterXSection())
# Output Bragg Diffraction
ConvertUnits(InputWorkspace=sam_corrected,
OutputWorkspace=sam_corrected,
Target="TOF",
EMode="Elastic")
ConvertToHistogram(InputWorkspace=sam_corrected,
OutputWorkspace=sam_corrected)
xmin, xmax = get_each_spectra_xmin_xmax(mtd[sam_corrected])
CropWorkspaceRagged(InputWorkspace=sam_corrected,
OutputWorkspace=sam_corrected,
Xmin=xmin,
Xmax=xmax)
xmin_rebin = min(xmin)
xmax_rebin = max(xmax)
tof_binning = "{xmin},-0.01,{xmax}".format(xmin=xmin_rebin,
xmax=xmax_rebin)
Rebin(InputWorkspace=sam_corrected,
OutputWorkspace=sam_corrected,
Params=tof_binning)
SaveGSS(InputWorkspace=sam_corrected,
Filename=os.path.join(os.path.abspath(OutputDir), title + ".gsa"),
SplitFiles=False,
Append=False,
MultiplyByBinWidth=True,
Format="SLOG",
ExtendedHeader=True)
return mtd[sam_corrected]
def PyExec(self):
in_Runs = self.getProperty("RunNumbers").value
progress = Progress(self, 0., .25, 3)
finalUnits = self.getPropertyValue("FinalUnits")
self.chunkSize = self.getProperty('MaxChunkSize').value
# default arguments for AlignAndFocusPowder
self.alignAndFocusArgs = {'Tmin': 0,
'TMax': 50000,
'RemovePromptPulseWidth': 1600,
'PreserveEvents': False,
'Dspacing': True, # binning parameters in d-space
'Params': self.getProperty("Binning").value,
}
# workspace for loading metadata only to be used in LoadDiffCal and
# CreateGroupingWorkspace
metaWS = None
# either type of file-based calibration is stored in the same variable
calib = self.getProperty("Calibration").value
detcalFile = None
if calib == "Calibration File":
metaWS = self._loadMetaWS(in_Runs[0])
LoadDiffCal(Filename=self.getPropertyValue("CalibrationFilename"),
WorkspaceName='SNAP',
InputWorkspace=metaWS,
MakeGroupingWorkspace=False, MakeMaskWorkspace=False)
self.alignAndFocusArgs['CalibrationWorkspace'] = 'SNAP_cal'
elif calib == 'DetCal File':
detcalFile = ','.join(self.getProperty('DetCalFilename').value)
progress.report('loaded calibration')
norm = self.getProperty("Normalization").value
if norm == "From Processed Nexus":
norm_File = self.getProperty("NormalizationFilename").value
normalizationWS = 'normWS'
LoadNexusProcessed(Filename=norm_File, OutputWorkspace=normalizationWS)
progress.report('loaded normalization')
elif norm == "From Workspace":
normalizationWS = str(self.getProperty("NormalizationWorkspace").value)
progress.report('')
else:
normalizationWS = None
progress.report('')
self.alignAndFocusArgs['GroupingWorkspace'] = self._generateGrouping(in_Runs[0], metaWS, progress)
self.alignAndFocusArgs['MaskWorkspace'] = self._getMaskWSname(in_Runs[0], metaWS) # can be empty string
if metaWS is not None:
DeleteWorkspace(Workspace=metaWS)
Process_Mode = self.getProperty("ProcessingMode").value
prefix = self.getProperty("OptionalPrefix").value
Tag = 'SNAP'
progStart = .25
progDelta = (1.-progStart)/len(in_Runs)
# --------------------------- PROCESS BACKGROUND ----------------------
if not self.getProperty('Background').isDefault:
progDelta = (1. - progStart) / (len(in_Runs) + 1) # redefine to account for background
background = 'SNAP_{}'.format(self.getProperty('Background').value)
self.log().notice("processing run background {}".format(background))
background, unfocussedBkgd = self._alignAndFocus(background,
background+'_bkgd_red',
detCalFilename=detcalFile,
withUnfocussed=(Process_Mode == 'Set-Up'),
progStart=progStart, progDelta=progDelta)
else:
background = None
unfocussedBkgd = ''
# --------------------------- REDUCE DATA -----------------------------
for i, runnumber in enumerate(in_Runs):
self.log().notice("processing run %s" % runnumber)
# put together output names
new_Tag = Tag
if len(prefix) > 0:
new_Tag = prefix + '_' + new_Tag
basename = '%s_%s_%s' % (new_Tag, runnumber, self.alignAndFocusArgs['GroupingWorkspace'])
self.log().warning('{}:{}:{}'.format(i, new_Tag, basename))
redWS, unfocussedWksp = self._alignAndFocus('SNAP_{}'.format(runnumber),
basename + '_red',
detCalFilename=detcalFile,
withUnfocussed=(Process_Mode == 'Set-Up'),
progStart=progStart, progDelta=progDelta*.5)
progStart += .5 * progDelta
# subtract the background if it was supplied
if background:
self.log().information('subtracting {} from {}'.format(background, redWS))
Minus(LHSWorkspace=redWS, RHSWorkspace=background, OutputWorkspace=redWS)
# intentionally don't subtract the unfocussed workspace since it hasn't been normalized by counting time
# the rest takes up .25 percent of the run processing
progress = Progress(self, progStart, progStart+.25*progDelta, 2)
# AlignAndFocusPowder leaves the data in time-of-flight
ConvertUnits(InputWorkspace=redWS, OutputWorkspace=redWS, Target='dSpacing', EMode='Elastic')
# Edit instrument geometry to make final workspace smaller on disk
det_table = PreprocessDetectorsToMD(Inputworkspace=redWS,
OutputWorkspace='__SNAP_det_table')
polar = np.degrees(det_table.column('TwoTheta'))
azi = np.degrees(det_table.column('Azimuthal'))
EditInstrumentGeometry(Workspace=redWS, L2=det_table.column('L2'),
Polar=polar, Azimuthal=azi)
mtd.remove('__SNAP_det_table')
progress.report('simplify geometry')
# AlignAndFocus doesn't necessarily rebin the data correctly
if Process_Mode == "Set-Up":
Rebin(InputWorkspace=unfocussedWksp, Params=self.alignAndFocusArgs['Params'],
Outputworkspace=unfocussedWksp)
if background:
Rebin(InputWorkspace=unfocussedBkgd, Params=self.alignAndFocusArgs['Params'],
Outputworkspace=unfocussedBkgd)
# normalize the data as requested
normalizationWS = self._generateNormalization(redWS, norm, normalizationWS)
normalizedWS = None
if normalizationWS is not None:
normalizedWS = basename + '_nor'
Divide(LHSWorkspace=redWS, RHSWorkspace=normalizationWS,
OutputWorkspace=normalizedWS)
ReplaceSpecialValues(Inputworkspace=normalizedWS,
OutputWorkspace=normalizedWS,
NaNValue='0', NaNError='0',
InfinityValue='0', InfinityError='0')
progress.report('normalized')
else:
progress.report()
# rename everything as appropriate and determine output workspace name
if normalizedWS is None:
outputWksp = redWS
else:
outputWksp = normalizedWS
if norm == "Extracted from Data" and Process_Mode == "Production":
DeleteWorkspace(Workspace=redWS)
DeleteWorkspace(Workspace=normalizationWS)
# Save requested formats - function checks that saving is requested
self._save(runnumber, basename, outputWksp)
# set workspace as an output so it gets history
ConvertUnits(InputWorkspace=str(outputWksp), OutputWorkspace=str(outputWksp), Target=finalUnits,
EMode='Elastic')
self._exportWorkspace('OutputWorkspace_' + str(outputWksp), outputWksp)
# declare some things as extra outputs in set-up
if Process_Mode != "Production":
propprefix = 'OutputWorkspace_{:d}_'.format(i)
propNames = [propprefix + it for it in ['d', 'norm', 'normalizer']]
wkspNames = ['%s_%s_d' % (new_Tag, runnumber),
basename + '_red',
'%s_%s_normalizer' % (new_Tag, runnumber)]
for (propName, wkspName) in zip(propNames, wkspNames):
self._exportWorkspace(propName, wkspName)
if background:
ConvertUnits(InputWorkspace=str(background), OutputWorkspace=str(background), Target=finalUnits,
EMode='Elastic')
prefix = 'OutputWorkspace_{}'.format(len(in_Runs))
propNames = [prefix + it for it in ['', '_d']]
wkspNames = [background, unfocussedBkgd]
for (propName, wkspName) in zip(propNames, wkspNames):
self._exportWorkspace(propName, wkspName)
def PyExec(self):
data = self.getProperty("InputWorkspace").value # [1~n]
bkg = self.getProperty("BackgroundWorkspace").value # [1~n]
cal = self.getProperty("CalibrationWorkspace").value # [1]
xMin = self.getProperty("XMin").value
xMax = self.getProperty("XMax").value
numberBins = self.getProperty("NumberBins").value
outWS = self.getPropertyValue("OutputWorkspace")
# NOTE:
# StringArrayProperty cannot be optional, so the background can only be passed in as a string
# or a list, which will be manually unpacked here
if bkg != "":
bkg = [
AnalysisDataService.retrieve(me)
for me in map(str.strip, bkg.split(","))
]
# NOTE:
# xMin and xMax are initialized as empty numpy.array (np.array([])).
_xMin, _xMax = self._locate_global_xlimit()
xMin = _xMin if xMin.size == 0 else xMin
xMax = _xMax if xMax.size == 0 else xMax
# BEGIN_FOR: prcess_spectra
for n, _wsn in enumerate(data):
_mskn = f"__mask_{n}" # calculated in previous loop
_ws = AnalysisDataService.retrieve(_wsn)
# resample spectra
_ws_resampled = ResampleX(
InputWorkspace=f"__ws_{n}",
XMin=xMin,
XMax=xMax,
NumberBins=numberBins,
EnableLogging=False,
)
# calibration
if cal is not None:
_ws_cal_resampled = self._resample_calibration(_ws, _mskn, xMin, xMax)
_ws_resampled = Divide(
LHSWorkspace=_ws_resampled,
RHSWorkspace=_ws_cal_resampled,
EnableLogging=False,
)
else:
_ws_cal_resampled = None
_ws_resampled = Scale(
InputWorkspace=_ws_resampled,
Factor=self._get_scale(cal) / self._get_scale(_ws),
EnableLogging=False,
)
# background
if bkg != "":
bgn = bkg[n] if isinstance(bkg, list) else bkg
_ws_bkg_resampled = self._resample_background(
bgn, _ws, _mskn, xMin, xMax, _ws_cal_resampled
)
_ws_resampled = Minus(
LHSWorkspace=_ws_resampled,
RHSWorkspace=_ws_bkg_resampled,
EnableLogging=False,
)
# conjoin
if n < 1:
CloneWorkspace(
InputWorkspace=_ws_resampled,
OutputWorkspace="__ws_conjoined",
EnableLogging=False,
)
else:
ConjoinWorkspaces(
InputWorkspace1="__ws_conjoined",
InputWorkspace2=_ws_resampled,
CheckOverlapping=False,
EnableLogging=False,
)
# END_FOR: prcess_spectra
# Step_3: sum all spectra
# ref: https://docs.mantidproject.org/nightly/algorithms/SumSpectra-v1.html
if cal is not None:
SumSpectra(
InputWorkspace="__ws_conjoined",
OutputWorkspace=outWS,
WeightedSum=True,
MultiplyBySpectra=False,
EnableLogging=False,
)
else:
SumSpectra(
InputWorkspace="__ws_conjoined",
OutputWorkspace=outWS,
WeightedSum=True,
MultiplyBySpectra=True,
EnableLogging=False,
)
self.setProperty("OutputWorkspace", outWS)
# Step_4: remove temp workspaces
[
DeleteWorkspace(ws, EnableLogging=False)
for ws in self.temp_workspace_list
if mtd.doesExist(ws)
]
def PyExec(self):
data = self.getProperty("InputWorkspace").value
cal = self.getProperty("CalibrationWorkspace").value
bkg = self.getProperty("BackgroundWorkspace").value
mask = self.getProperty("MaskWorkspace").value
target = self.getProperty("Target").value
eFixed = self.getProperty("EFixed").value
xMin = self.getProperty("XMin").value
xMax = self.getProperty("XMax").value
numberBins = self.getProperty("NumberBins").value
normaliseBy = self.getProperty("NormaliseBy").value
maskAngle = self.getProperty("MaskAngle").value
outWS = self.getPropertyValue("OutputWorkspace")
data_scale = 1
cal_scale = 1
bkg_scale = 1
if normaliseBy == "Monitor":
data_scale = data.run().getProtonCharge()
elif normaliseBy == "Time":
data_scale = data.run().getLogData('duration').value
ExtractMask(data, OutputWorkspace='__mask_tmp', EnableLogging=False)
if maskAngle != Property.EMPTY_DBL:
MaskAngle(Workspace='__mask_tmp',
MinAngle=maskAngle,
Angle='Phi',
EnableLogging=False)
if mask is not None:
BinaryOperateMasks(InputWorkspace1='__mask_tmp',
InputWorkspace2=mask,
OperationType='OR',
OutputWorkspace='__mask_tmp',
EnableLogging=False)
ExtractUnmaskedSpectra(InputWorkspace=data,
MaskWorkspace='__mask_tmp',
OutputWorkspace='__data_tmp',
EnableLogging=False)
if isinstance(mtd['__data_tmp'], IEventWorkspace):
Integration(InputWorkspace='__data_tmp',
OutputWorkspace='__data_tmp',
EnableLogging=False)
ConvertSpectrumAxis(InputWorkspace='__data_tmp',
Target=target,
EFixed=eFixed,
OutputWorkspace=outWS,
EnableLogging=False)
Transpose(InputWorkspace=outWS,
OutputWorkspace=outWS,
EnableLogging=False)
ResampleX(InputWorkspace=outWS,
OutputWorkspace=outWS,
XMin=xMin,
XMax=xMax,
NumberBins=numberBins,
EnableLogging=False)
if cal is not None:
ExtractUnmaskedSpectra(InputWorkspace=cal,
MaskWorkspace='__mask_tmp',
OutputWorkspace='__cal_tmp',
EnableLogging=False)
if isinstance(mtd['__cal_tmp'], IEventWorkspace):
Integration(InputWorkspace='__cal_tmp',
OutputWorkspace='__cal_tmp',
EnableLogging=False)
CopyInstrumentParameters(data, '__cal_tmp', EnableLogging=False)
ConvertSpectrumAxis(InputWorkspace='__cal_tmp',
Target=target,
EFixed=eFixed,
OutputWorkspace='__cal_tmp',
EnableLogging=False)
Transpose(InputWorkspace='__cal_tmp',
OutputWorkspace='__cal_tmp',
EnableLogging=False)
ResampleX(InputWorkspace='__cal_tmp',
OutputWorkspace='__cal_tmp',
XMin=xMin,
XMax=xMax,
NumberBins=numberBins,
EnableLogging=False)
Divide(LHSWorkspace=outWS,
RHSWorkspace='__cal_tmp',
OutputWorkspace=outWS,
EnableLogging=False)
if normaliseBy == "Monitor":
cal_scale = cal.run().getProtonCharge()
elif normaliseBy == "Time":
cal_scale = cal.run().getLogData('duration').value
Scale(InputWorkspace=outWS,
OutputWorkspace=outWS,
Factor=cal_scale / data_scale,
EnableLogging=False)
if bkg is not None:
ExtractUnmaskedSpectra(InputWorkspace=bkg,
MaskWorkspace='__mask_tmp',
OutputWorkspace='__bkg_tmp',
EnableLogging=False)
if isinstance(mtd['__bkg_tmp'], IEventWorkspace):
Integration(InputWorkspace='__bkg_tmp',
OutputWorkspace='__bkg_tmp',
EnableLogging=False)
CopyInstrumentParameters(data, '__bkg_tmp', EnableLogging=False)
ConvertSpectrumAxis(InputWorkspace='__bkg_tmp',
Target=target,
EFixed=eFixed,
OutputWorkspace='__bkg_tmp',
EnableLogging=False)
Transpose(InputWorkspace='__bkg_tmp',
OutputWorkspace='__bkg_tmp',
EnableLogging=False)
ResampleX(InputWorkspace='__bkg_tmp',
OutputWorkspace='__bkg_tmp',
XMin=xMin,
XMax=xMax,
NumberBins=numberBins,
EnableLogging=False)
if cal is not None:
Divide(LHSWorkspace='__bkg_tmp',
RHSWorkspace='__cal_tmp',
OutputWorkspace='__bkg_tmp',
EnableLogging=False)
if normaliseBy == "Monitor":
bkg_scale = bkg.run().getProtonCharge()
elif normaliseBy == "Time":
bkg_scale = bkg.run().getLogData('duration').value
Scale(InputWorkspace='__bkg_tmp',
OutputWorkspace='__bkg_tmp',
Factor=cal_scale / bkg_scale,
EnableLogging=False)
Scale(InputWorkspace='__bkg_tmp',
OutputWorkspace='__bkg_tmp',
Factor=self.getProperty('BackgroundScale').value,
EnableLogging=False)
Minus(LHSWorkspace=outWS,
RHSWorkspace='__bkg_tmp',
OutputWorkspace=outWS,
EnableLogging=False)
self.setProperty("OutputWorkspace", outWS)
# remove temp workspaces
[
DeleteWorkspace(ws, EnableLogging=False)
for ws in self.temp_workspace_list if mtd.doesExist(ws)
]
def PyExec(self):
data = self._expand_groups()
bkg = self.getProperty(
"BackgroundWorkspace").valueAsStr # same background for all
cal = self.getProperty(
"CalibrationWorkspace").value # same calibration for all
numberBins = self.getProperty("NumberBins").value
outWS = self.getPropertyValue("OutputWorkspace")
summing = self.getProperty("Sum").value # [Yes or No]
# convert all of the input workspaces into spectrum of "target" units (generally angle)
data, masks = self._convert_data(data)
# determine x-range
xMin, xMax = self._locate_global_xlimit(data)
# BEGIN_FOR: prcess_spectra
for n, (_wsn, _mskn) in enumerate(zip(data, masks)):
# resample spectra
ResampleX(
InputWorkspace=_wsn,
OutputWorkspace=_wsn,
XMin=xMin,
XMax=xMax,
NumberBins=numberBins,
EnableLogging=False,
)
# calibration
if cal is not None:
_ws_cal_resampled = self._resample_calibration(
_wsn, _mskn, xMin, xMax)
Divide(
LHSWorkspace=_wsn,
RHSWorkspace=_ws_cal_resampled,
OutputWorkspace=_wsn,
EnableLogging=False,
)
else:
_ws_cal_resampled = None
Scale(
InputWorkspace=_wsn,
OutputWorkspace=_wsn,
Factor=self._get_scale(cal) / self._get_scale(_wsn),
EnableLogging=False,
)
# background
if bkg:
_ws_bkg_resampled = self._resample_background(
bkg, _wsn, _mskn, xMin, xMax, _ws_cal_resampled)
Minus(
LHSWorkspace=_wsn,
RHSWorkspace=_ws_bkg_resampled,
OutputWorkspace=_wsn,
EnableLogging=False,
)
if summing:
# conjoin
if n < 1:
RenameWorkspace(
InputWorkspace=_wsn,
OutputWorkspace="__ws_conjoined",
EnableLogging=False,
)
else:
# this adds to `InputWorkspace1`
ConjoinWorkspaces(
InputWorkspace1="__ws_conjoined",
InputWorkspace2=_wsn,
CheckOverlapping=False,
EnableLogging=False,
)
# END_FOR: prcess_spectra
# Step_3: sum all spectra
# ref: https://docs.mantidproject.org/nightly/algorithms/SumSpectra-v1.html
if summing:
if cal is not None:
outWS = SumSpectra(
InputWorkspace="__ws_conjoined",
OutputWorkspace=outWS,
WeightedSum=True,
MultiplyBySpectra=not bool(cal),
EnableLogging=False,
)
else:
outWS = SumSpectra(
InputWorkspace="__ws_conjoined",
OutputWorkspace=outWS,
WeightedSum=True,
MultiplyBySpectra=True,
EnableLogging=False,
)
else:
if len(data) == 1:
outWS = RenameWorkspace(InputWorkspace=data[0],
OutputWorkspace=outWS)
else:
outWS = GroupWorkspaces(InputWorkspaces=data,
OutputWorkspace=outWS)
self.setProperty("OutputWorkspace", outWS)
# Step_4: remove temp workspaces
[
DeleteWorkspace(ws, EnableLogging=False)
for ws in self.temp_workspace_list if mtd.doesExist(ws)
]
def PyExec(self):
_background = bool(self.getProperty("Background").value)
_load_inst = bool(self.getProperty("LoadInstrument").value)
_norm_current = bool(self.getProperty("NormaliseByCurrent").value)
_detcal = bool(self.getProperty("DetCal").value)
_masking = bool(self.getProperty("MaskFile").value)
_grouping = bool(self.getProperty("GroupingFile").value)
_anvred = bool(self.getProperty("SphericalAbsorptionCorrection").value)
_SA_name = self.getPropertyValue("SolidAngleOutputWorkspace")
_Flux_name = self.getPropertyValue("FluxOutputWorkspace")
XMin = self.getProperty("MomentumMin").value
XMax = self.getProperty("MomentumMax").value
rebin_param = ','.join([str(XMin), str(XMax), str(XMax)])
Load(Filename=self.getPropertyValue("Filename"),
OutputWorkspace='__van',
FilterByTofMin=self.getProperty("FilterByTofMin").value,
FilterByTofMax=self.getProperty("FilterByTofMax").value)
if _norm_current:
NormaliseByCurrent(InputWorkspace='__van', OutputWorkspace='__van')
if _background:
Load(Filename=self.getProperty("Background").value,
OutputWorkspace='__bkg',
FilterByTofMin=self.getProperty("FilterByTofMin").value,
FilterByTofMax=self.getProperty("FilterByTofMax").value)
if _norm_current:
NormaliseByCurrent(InputWorkspace='__bkg',
OutputWorkspace='__bkg')
else:
pc_van = mtd['__van'].run().getProtonCharge()
pc_bkg = mtd['__bkg'].run().getProtonCharge()
mtd['__bkg'] *= pc_van / pc_bkg
mtd['__bkg'] *= self.getProperty('BackgroundScale').value
Minus(LHSWorkspace='__van',
RHSWorkspace='__bkg',
OutputWorkspace='__van')
DeleteWorkspace('__bkg')
if _load_inst:
LoadInstrument(Workspace='__van',
Filename=self.getProperty("LoadInstrument").value,
RewriteSpectraMap=False)
if _detcal:
LoadIsawDetCal(InputWorkspace='__van',
Filename=self.getProperty("DetCal").value)
if _masking:
LoadMask(Instrument=mtd['__van'].getInstrument().getName(),
InputFile=self.getProperty("MaskFile").value,
OutputWorkspace='__mask')
MaskDetectors(Workspace='__van', MaskedWorkspace='__mask')
DeleteWorkspace('__mask')
ConvertUnits(InputWorkspace='__van',
OutputWorkspace='__van',
Target='Momentum')
Rebin(InputWorkspace='__van',
OutputWorkspace='__van',
Params=rebin_param)
CropWorkspace(InputWorkspace='__van',
OutputWorkspace='__van',
XMin=XMin,
XMax=XMax)
if _anvred:
AnvredCorrection(InputWorkspace='__van',
OutputWorkspace='__van',
LinearScatteringCoef=self.getProperty(
"LinearScatteringCoef").value,
LinearAbsorptionCoef=self.getProperty(
"LinearAbsorptionCoef").value,
Radius=self.getProperty("Radius").value,
OnlySphericalAbsorption='1',
PowerLambda='0')
# Create solid angle
Rebin(InputWorkspace='__van',
OutputWorkspace=_SA_name,
Params=rebin_param,
PreserveEvents=False)
# Create flux
if _grouping:
GroupDetectors(InputWorkspace='__van',
OutputWorkspace='__van',
MapFile=self.getProperty("GroupingFile").value)
else:
SumSpectra(InputWorkspace='__van', OutputWorkspace='__van')
Rebin(InputWorkspace='__van',
OutputWorkspace='__van',
Params=rebin_param)
flux = mtd['__van']
for i in range(flux.getNumberHistograms()):
el = flux.getSpectrum(i)
if flux.readY(i)[0] > 0:
el.divide(flux.readY(i)[0], flux.readE(i)[0])
SortEvents(InputWorkspace='__van', SortBy="X Value")
IntegrateFlux(InputWorkspace='__van',
OutputWorkspace=_Flux_name,
NPoints=10000)
DeleteWorkspace('__van')
self.setProperty("SolidAngleOutputWorkspace", mtd[_SA_name])
self.setProperty("FluxOutputWorkspace", mtd[_Flux_name])