def create_grouping_workspace_from_spectra_list(self):
"""
Create grouping workspace for ROI defined as a list of spectrum numbers
"""
grp_ws, _, _ = CreateGroupingWorkspace(
InstrumentName=self.instrument,
OutputWorkspace=GROUP_WS_NAMES[self.group])
for spec in self.spectra_list:
det_ids = grp_ws.getDetectorIDs(spec - 1)
grp_ws.setValue(det_ids[0], 1)
self.group_ws = grp_ws
def create_grouping_workspace_from_calfile(self):
"""
Create grouping workspace for ROI defined in .cal file
"""
grp_ws, _, _ = CreateGroupingWorkspace(
InstrumentName=self.instrument,
OldCalFilename=self.cal_filepath,
OutputWorkspace=GROUP_WS_NAMES[self.group])
self.group_ws = grp_ws
def group_pixels_2theta(vulcan_ws_name, tth_group_ws_name, start_iws, end_iws,
two_theta_bin_range, two_theta_step):
# create group workspace
CreateGroupingWorkspace(InputWorkspace=vulcan_ws_name, GroupDetectorsBy='All',
OutputWorkspace=tth_group_ws_name)
# Get workspace
vulcan_ws = mantid_helper.retrieve_workspace(vulcan_ws_name, True)
group_ws = mantid_helper.retrieve_workspace(tth_group_ws_name, True)
# Calculate 2theta for each pixels that is interested
two_theta_array = numpy.arange(two_theta_bin_range[0], two_theta_bin_range[1] + two_theta_step,
two_theta_step, dtype='float')
num_2theta = two_theta_array.shape[0]
num_pixels_array = numpy.zeros(shape=two_theta_array.shape, dtype='int')
# source and sample position
source = vulcan_ws.getInstrument().getSource().getPos()
sample = vulcan_ws.getInstrument().getSample().getPos()
# Calculate 2theta for each detectors
for iws in range(0, vulcan_ws.getNumberHistograms()):
if iws < start_iws or iws >= end_iws:
# set to group 0 to ignore
group_ws.dataY(iws)[0] = 0
else:
# interested
det_i = vulcan_ws.getDetector(iws).getPos()
two_theta_i = (det_i - sample).angle(sample - source) * 180. / numpy.pi
if two_theta_i < two_theta_array[0] or two_theta_i >= two_theta_array[-1]:
group_ws.dataY(iws)[0] = 0
elif two_theta_i == two_theta_array[0]:
group_ws.dataY(iws)[0] = 1
num_pixels_array[0] += 1
else:
i_2theta = numpy.searchsorted(two_theta_array, [two_theta_i])[0]
if i_2theta <= 0 or i_2theta >= num_2theta:
raise RuntimeError('Programming error!')
group_ws.dataY(iws)[0] = i_2theta
num_pixels_array[i_2theta-1] += 1
# END-IF-ELSE
# END-FOR
# deal with zero-count-instance
num_pixels_array[numpy.where(num_pixels_array < 0.1)] = -1
return two_theta_array, group_ws, num_pixels_array
def _generateGrouping(self, runnumber, metaWS, progress):
group_to_real = {'Banks': 'Group', 'Modules': 'bank', '2_4 Grouping': '2_4Grouping'}
group = self.getProperty('GroupDetectorsBy').value
real_name = group_to_real.get(group, group)
if not mtd.doesExist(group):
if group == '2_4 Grouping':
group = '2_4_Grouping'
if not metaWS :
metaWS = self._loadMetaWS(runnumber)
CreateGroupingWorkspace(InputWorkspace=metaWS, GroupDetectorsBy=real_name,
OutputWorkspace=group)
progress.report('create grouping')
else:
progress.report()
return group
def create_test_ws_and_group():
myFunc = "name=Gaussian, PeakCentre=2, Height=100, Sigma=0.01;" + \
"name=Gaussian, PeakCentre=1, Height=100, Sigma=0.01;" + \
"name=Gaussian, PeakCentre=4, Height=100, Sigma=0.01"
ws = CreateSampleWorkspace("Event",
"User Defined",
myFunc,
BankPixelWidth=1,
XUnit='dSpacing',
XMax=5,
BinWidth=0.001,
NumEvents=100000,
NumBanks=8)
for n in range(1, 5):
MoveInstrumentComponent(ws,
ComponentName=f'bank{n}',
X=1 + n / 10,
Y=0,
Z=1 + n / 10,
RelativePosition=False)
MoveInstrumentComponent(ws,
ComponentName=f'bank{n+4}',
X=2 + n / 10,
Y=0,
Z=2 + n / 10,
RelativePosition=False)
MaskDetectors(ws, WorkspaceIndexList=[3, 7])
ws = ScaleX(ws, Factor=1.05, IndexMin=1, IndexMax=1)
ws = ScaleX(ws, Factor=0.95, IndexMin=2, IndexMax=2)
ws = ScaleX(ws, Factor=1.05, IndexMin=4, IndexMax=6)
ws = ScaleX(ws, Factor=1.02, IndexMin=5, IndexMax=5)
ws = ScaleX(ws, Factor=0.98, IndexMin=6, IndexMax=6)
ws = Rebin(ws, '0,0.001,5')
ws = ConvertUnits(ws, Target='TOF')
groups, _, _ = CreateGroupingWorkspace(InputWorkspace=ws,
ComponentName='basic_rect',
CustomGroupingString='1-4,5-8')
return ws, groups
def runTest(self):
# 11MB file, process in 4 chunks
kwargs = {
'RunNumbers': '45874',
'GroupDetectorsBy': 'Banks',
'Binning': [.5, -.004, 7],
'MaxChunkSize': .01
}
# create grouping for two output spectra
CreateGroupingWorkspace(InstrumentFilename='SNAP_Definition.xml',
GroupDetectorsBy='Group',
OutputWorkspace='SNAP_grouping')
_assert_reduction_configuration(kwargs)
SNAPReduce(**kwargs)
RenameWorkspace(InputWorkspace='SNAP_45874_Banks_red',
OutputWorkspace='with_chunks')
# process without chunks
kwargs['MaxChunkSize'] = 0
SNAPReduce(**kwargs)
RenameWorkspace(InputWorkspace='SNAP_45874_Banks_red',
OutputWorkspace='no_chunks')
def create_bank_grouping_workspace(self):
"""
Create grouping workspace for ROI corresponding to one or more banks
"""
ws_name = GROUP_WS_NAMES[self.group]
grp_ws = None
try:
grp_ws = LoadDetectorsGroupingFile(InputFile=path.join(
CALIB_DIR, GROUP_FILES[self.group]),
OutputWorkspace=ws_name)
except ValueError:
logger.notice(
"Grouping file not found in user directories - creating one")
if self.group.banks and self.group != GROUP.TEXTURE20 and self.group != GROUP.TEXTURE30:
grp_ws, _, _ = CreateGroupingWorkspace(
InstrumentName=self.instrument,
OutputWorkspace=ws_name,
GroupNames=GROUP_BANK_ARGS[self.group])
if grp_ws:
self.group_ws = grp_ws
else:
raise ValueError(
"Could not find or create grouping requested - make sure the directory of the grouping.xml"
" files is on the path")
def PyExec(self):
# Retrieve all relevant notice
in_Runs = self.getProperty("RunNumbers").value
maskWSname = self._getMaskWSname()
# either type of file-based calibration is stored in the same variable
calib = self.getProperty("Calibration").value
if calib == "Calibration File":
cal_File = self.getProperty("CalibrationFilename").value
elif calib == 'DetCal File':
cal_File = self.getProperty('DetCalFilename').value
cal_File = ','.join(cal_File)
else:
cal_File = None
params = self.getProperty("Binning").value
norm = self.getProperty("Normalization").value
if norm == "From Processed Nexus":
norm_File = self.getProperty("NormalizationFilename").value
LoadNexusProcessed(Filename=norm_File, OutputWorkspace='normWS')
normWS = 'normWS'
elif norm == "From Workspace":
normWS = str(self.getProperty("NormalizationWorkspace").value)
else:
normWS = None
group_to_real = {
'Banks': 'Group',
'Modules': 'bank',
'2_4 Grouping': '2_4Grouping'
}
group = self.getProperty('GroupDetectorsBy').value
real_name = group_to_real.get(group, group)
if not mtd.doesExist(group):
if group == '2_4 Grouping':
group = '2_4_Grouping'
CreateGroupingWorkspace(InstrumentName='SNAP',
GroupDetectorsBy=real_name,
OutputWorkspace=group)
Process_Mode = self.getProperty("ProcessingMode").value
prefix = self.getProperty("OptionalPrefix").value
# --------------------------- REDUCE DATA -----------------------------
Tag = 'SNAP'
for r in in_Runs:
self.log().notice("processing run %s" % r)
self.log().information(str(self.get_IPTS_Local(r)))
if self.getProperty("LiveData").value:
Tag = 'Live'
LoadPreNexusLive(Instrument='SNAP', OutputWorkspace='WS')
else:
Load(Filename='SNAP' + str(r), OutputWorkspace='WS')
NormaliseByCurrent(InputWorkspace='WS', OutputWorkspace='WS')
CompressEvents(InputWorkspace='WS', OutputWorkspace='WS')
CropWorkspace(InputWorkspace='WS',
OutputWorkspace='WS',
XMax=50000)
RemovePromptPulse(InputWorkspace='WS',
OutputWorkspace='WS',
Width='1600',
Frequency='60.4')
if maskWSname is not None:
MaskDetectors(Workspace='WS', MaskedWorkspace=maskWSname)
self._alignAndFocus(params, calib, cal_File, group)
normWS = self._generateNormalization('WS_red', norm, normWS)
WS_nor = None
if normWS is not None:
WS_nor = 'WS_nor'
Divide(LHSWorkspace='WS_red',
RHSWorkspace=normWS,
OutputWorkspace='WS_nor')
ReplaceSpecialValues(Inputworkspace='WS_nor',
OutputWorkspace='WS_nor',
NaNValue='0',
NaNError='0',
InfinityValue='0',
InfinityError='0')
new_Tag = Tag
if len(prefix) > 0:
new_Tag += '_' + prefix
# Edit instrument geomety to make final workspace smaller on disk
det_table = PreprocessDetectorsToMD(
Inputworkspace='WS_red', OutputWorkspace='__SNAP_det_table')
polar = np.degrees(det_table.column('TwoTheta'))
azi = np.degrees(det_table.column('Azimuthal'))
EditInstrumentGeometry(Workspace='WS_red',
L2=det_table.column('L2'),
Polar=polar,
Azimuthal=azi)
if WS_nor is not None:
EditInstrumentGeometry(Workspace='WS_nor',
L2=det_table.column('L2'),
Polar=polar,
Azimuthal=azi)
mtd.remove('__SNAP_det_table')
# Save requested formats
basename = '%s_%s_%s' % (new_Tag, r, group)
self._save(r, basename, norm)
# temporary workspace no longer needed
DeleteWorkspace(Workspace='WS')
# rename everything as appropriate and determine output workspace name
RenameWorkspace(Inputworkspace='WS_d',
OutputWorkspace='%s_%s_d' % (new_Tag, r))
RenameWorkspace(Inputworkspace='WS_red',
OutputWorkspace=basename + '_red')
if norm == 'None':
outputWksp = basename + '_red'
else:
outputWksp = basename + '_nor'
RenameWorkspace(Inputworkspace='WS_nor',
OutputWorkspace=basename + '_nor')
if norm == "Extracted from Data":
RenameWorkspace(Inputworkspace='peak_clip_WS',
OutputWorkspace='%s_%s_normalizer' %
(new_Tag, r))
# delte some things in production
if Process_Mode == "Production":
DeleteWorkspace(Workspace='%s_%s_d' %
(new_Tag, r)) # was 'WS_d'
if norm != "None":
DeleteWorkspace(Workspace=basename +
'_red') # was 'WS_red'
if norm == "Extracted from Data":
DeleteWorkspace(Workspace='%s_%s_normalizer' %
(new_Tag, r)) # was 'peak_clip_WS'
propertyName = 'OutputWorkspace_' + str(outputWksp)
self.declareProperty(
WorkspaceProperty(propertyName, outputWksp, Direction.Output))
self.setProperty(propertyName, outputWksp)
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 process_json(json_filename):
"""This will read a json file, process the data and save the calibration.
Only ``Calibrant`` and ``Groups`` are required.
An example input showing every possible options is:
.. code-block:: JSON
{
"Calibrant": "12345",
"Groups": "/path/to/groups.xml",
"Mask": "/path/to/mask.xml",
"Instrument": "NOM",
"Date" : "2019_09_04",
"SampleEnvironment": "shifter",
"PreviousCalibration": "/path/to/cal.h5",
"CalDirectory": "/path/to/output_directory",
"CrossCorrelate": {"Step": 0.001,
"DReference: 1.5,
"Xmin": 1.0,
"Xmax": 3.0,
"MaxDSpaceShift": 0.25},
"PDCalibration": {"PeakPositions": [1, 2, 3],
"TofBinning": (300,0.001,16666),
"PeakFunction": 'Gaussian',
"PeakWindow": 0.1,
"PeakWidthPercent": 0.001}
}
"""
with open(json_filename) as json_file:
args = json.load(json_file)
calibrant_file = args.get('CalibrantFile', None)
if calibrant_file is None:
calibrant = args['Calibrant']
groups = args['Groups']
out_groups_by = args.get('OutputGroupsBy', 'Group')
sample_env = args.get('SampleEnvironment', 'UnknownSampleEnvironment')
mask = args.get('Mask')
instrument = args.get('Instrument', 'NOM')
cc_kwargs = args.get('CrossCorrelate', {})
pdcal_kwargs = args.get('PDCalibration', {})
previous_calibration = args.get('PreviousCalibration')
date = str(args.get('Date', datetime.datetime.now().strftime('%Y_%m_%d')))
caldirectory = str(args.get('CalDirectory', os.path.abspath('.')))
if calibrant_file is not None:
ws = Load(calibrant_file)
calibrant = ws.getRun().getProperty('run_number').value
else:
filename = f'{instrument}_{calibrant}'
ws = Load(filename)
calfilename = f'{caldirectory}/{instrument}_{calibrant}_{date}_{sample_env}.h5'
logger.notice(f'going to create calibration file: {calfilename}')
groups = LoadDetectorsGroupingFile(groups, InputWorkspace=ws)
if mask:
mask = LoadMask(instrument, mask)
MaskDetectors(ws, MaskedWorkspace=mask)
if previous_calibration:
previous_calibration = LoadDiffCal(previous_calibration,
MakeGroupingWorkspace=False,
MakeMaskWorkspace=False)
diffcal = do_group_calibration(ws,
groups,
previous_calibration,
cc_kwargs=cc_kwargs,
pdcal_kwargs=pdcal_kwargs)
mask = mtd['group_calibration_pd_diffcal_mask']
CreateGroupingWorkspace(InputWorkspace=ws,
GroupDetectorsBy=out_groups_by,
OutputWorkspace='out_groups')
SaveDiffCal(CalibrationWorkspace=diffcal,
MaskWorkspace=mask,
GroupingWorkspace=mtd['out_groups'],
Filename=calfilename)
def PyExec(self):
temporary_workspaces = []
self.temp_ws = temp_workspace_generator(
temporary_workspaces) # name generator for temporary workpaces
prefix_output = self.getProperty('OutputWorkspacesPrefix').value
progress_percent_start, progress_percent_end, reports_count = 0.0, 0.01, 5
progress = Progress(self, progress_percent_start, progress_percent_end,
reports_count)
input_workspace = self.getPropertyValue(
'InputWorkspace') # name of the input workspace
adjustment_diagnostics = list(
) # list workspace names that analyze the orientation of the banks
# Create a grouping workspace whereby we group detectors by banks
grouping_workspace = self.temp_ws() # a temporary name
CreateGroupingWorkspace(InputWorkspace=input_workspace,
OutputWorkspace=grouping_workspace,
GroupDetectorsBy='bank')
# Remove delayed emission time from the moderator
kwargs = dict(InputWorkspace=input_workspace,
Emode='Elastic',
OutputWorkspace=input_workspace)
self.run_algorithm('ModeratorTzero',
0,
0.02,
soft_crash=True,
**kwargs)
progress.report('ModeratorTzero has been applied')
# Find dSpacing to TOF conversion DIFC parameter
difc_table = f'{prefix_output}PDCalibration_difc'
diagnostics_workspaces = prefix_output + 'PDCalibration_diagnostics' # group workspace
kwargs = dict(InputWorkspace=input_workspace,
TofBinning=self.getProperty('TofBinning').value,
PeakFunction=self.getProperty('PeakFunction').value,
PeakPositions=self.getProperty('PeakPositions').value,
CalibrationParameters='DIFC',
OutputCalibrationTable=difc_table,
DiagnosticWorkspaces=diagnostics_workspaces)
PDCalibration(**kwargs)
progress.report('PDCalibration has been applied')
# Create one spectra in d-spacing for each bank using the original instrument geometry
self.fitted_in_dspacing(
fitted_in_tof=prefix_output + 'PDCalibration_diagnostics_fitted',
workspace_with_instrument=input_workspace,
output_workspace=prefix_output + 'PDCalibration_peaks_original',
grouping_workspace=grouping_workspace)
adjustment_diagnostics.append(prefix_output +
'PDCalibration_peaks_original')
# Find the peak centers in TOF units, for the peaks found at each pixel
peak_centers_in_tof = prefix_output + 'PDCalibration_diagnostics_tof'
self.centers_in_tof(
prefix_output + 'PDCalibration_diagnostics_dspacing', difc_table,
peak_centers_in_tof)
mtd[diagnostics_workspaces].add(peak_centers_in_tof)
# Find the Histogram of peak deviations (in d-spacing units)
# for each bank, using the original instrument geometry
self.histogram_peak_deviations(
prefix_output + 'PDCalibration_diagnostics_tof', input_workspace,
prefix_output + 'peak_deviations_original', grouping_workspace)
adjustment_diagnostics.append(prefix_output +
'peak_deviations_original')
# repeat with percent peak deviations for each bank, using the adjusted instrument geometry
self.histogram_peak_deviations(
prefix_output + 'PDCalibration_diagnostics_tof',
input_workspace,
prefix_output + 'percent_peak_deviations_original',
grouping_workspace,
deviation_params=[-10, 0.01, 10],
percent_deviations=True)
adjustment_diagnostics.append(prefix_output +
'percent_peak_deviations_original')
# store the DIFC and DIFC_mask workspace created by PDCalibration in the diagnostics workspace
mtd[diagnostics_workspaces].add(difc_table)
mtd[diagnostics_workspaces].add(difc_table + '_mask')
adjustments_table_name = f'{prefix_output}adjustments'
# Adjust the position of the source along the beam (Z) axis
# The instrument in `input_workspace` is adjusted in-place
if self.getProperty('AdjustSource').value is True:
dz = self.getProperty('SourceMaxTranslation').value
kwargs = dict(
InputWorkspace=input_workspace,
OutputWorkspace=input_workspace,
PeakCentersTofTable=peak_centers_in_tof,
PeakPositions=self.getProperty('PeakPositions').value,
MaskWorkspace=f'{difc_table}_mask',
FitSourcePosition=True,
FitSamplePosition=False,
Zposition=True,
MinZPosition=-dz,
MaxZPosition=dz,
Minimizer='L-BFGS-B')
self.run_algorithm('AlignComponents', 0.1, 0.2, **kwargs)
else:
# Impose the fixed position of the source and save into the adjustments table
self._fixed_source_set_and_table(adjustments_table_name)
# Translate and rotate the each bank, only after the source has been adjusted
# The instrument in `input_workspace` is adjusted in-place
# Translation options to AlignComponents
dt = self.getProperty('ComponentMaxTranslation'
).value # maximum translation along either axis
move_y = False if self.getProperty('FixY').value is True else True
kwargs_transl = dict(Xposition=True,
MinXPosition=-dt,
MaxXPosition=dt,
Yposition=move_y,
MinYPosition=-dt,
MaxYPosition=dt,
Zposition=True,
MinZPosition=-dt,
MaxZPosition=dt)
# Rotation options for AlignComponents
dr = self.getProperty(
'ComponentMaxRotation').value # maximum rotation along either axis
rot_z = False if self.getProperty('FixYaw').value is True else True
kwargs_rotat = dict(AlphaRotation=True,
MinAlphaRotation=-dr,
MaxAlphaRotation=dr,
BetaRotation=True,
MinBetaRotation=-dr,
MaxBetaRotation=dr,
GammaRotation=rot_z,
MinGammaRotation=-dr,
MaxGammaRotation=dr,
EulerConvention='YXZ')
# Remaining options for AlignComponents
displacements_table_name = f'{prefix_output}displacements'
kwargs = dict(InputWorkspace=input_workspace,
OutputWorkspace=input_workspace,
PeakCentersTofTable=peak_centers_in_tof,
PeakPositions=self.getProperty('PeakPositions').value,
MaskWorkspace=f'{difc_table}_mask',
AdjustmentsTable=adjustments_table_name + '_banks',
DisplacementsTable=displacements_table_name,
FitSourcePosition=False,
FitSamplePosition=False,
ComponentList=self.getProperty('ComponentList').value,
Minimizer=self.getProperty('Minimizer').value,
MaxIterations=self.getProperty('MaxIterations').value)
self.run_algorithm('AlignComponents', 0.2, 0.97, **kwargs,
**kwargs_transl, **kwargs_rotat)
progress.report('AlignComponents has been applied')
# AlignComponents produces two unwanted workspaces
temporary_workspaces.append('calWS')
# Append the banks table to the source table, then delete the banks table.
self._append_second_to_first(adjustments_table_name,
adjustments_table_name + '_banks')
# Create one spectra in d-spacing for each bank using the adjusted instrument geometry.
# The spectra can be compare to those of prefix_output + 'PDCalibration_peaks_original'
self.fitted_in_dspacing(
fitted_in_tof=prefix_output + 'PDCalibration_diagnostics_fitted',
workspace_with_instrument=input_workspace,
output_workspace=prefix_output + 'PDCalibration_peaks_adjusted',
grouping_workspace=grouping_workspace)
adjustment_diagnostics.append(prefix_output +
'PDCalibration_peaks_adjusted')
# Find the Histogram of peak deviations (in d-spacing units)
# for each bank, using the adjusted instrument geometry
self.histogram_peak_deviations(
prefix_output + 'PDCalibration_diagnostics_tof', input_workspace,
prefix_output + 'peak_deviations_adjusted', grouping_workspace)
adjustment_diagnostics.append(prefix_output +
'peak_deviations_adjusted')
# repeat with percent peak deviations for each bank, using the adjusted instrument geometry
self.histogram_peak_deviations(
prefix_output + 'PDCalibration_diagnostics_tof',
input_workspace,
prefix_output + 'percent_peak_deviations_adjusted',
grouping_workspace,
deviation_params=[-10, 0.01, 10],
percent_deviations=True)
adjustment_diagnostics.append(prefix_output +
'percent_peak_deviations_adjusted')
# summarize the changes observed in the histogram of percent peak deviations
self.peak_deviations_summarize(
prefix_output + 'percent_peak_deviations_original',
prefix_output + 'percent_peak_deviations_adjusted',
prefix_output + 'percent_peak_deviations_summary')
adjustment_diagnostics.append(prefix_output +
'percent_peak_deviations_summary')
# Create a WorkspaceGroup with the orientation diagnostics
GroupWorkspaces(InputWorkspaces=adjustment_diagnostics,
OutputWorkspace=prefix_output +
'bank_adjustment_diagnostics')
# clean up at the end (only happens if algorithm completes sucessfully)
[
DeleteWorkspace(name) for name in temporary_workspaces
if AnalysisDataService.doesExist(name)
]
def PyExec(self):
runs = self.getProperty("Filename").value
if not runs:
ipts = self.getProperty("IPTS").value
runs = ['/HFIR/HB2C/IPTS-{}/nexus/HB2C_{}.nxs.h5'.format(ipts, run) for run in self.getProperty("RunNumbers").value]
grouping = self.getProperty("Grouping").value
if grouping == 'None':
grouping = 1
else:
grouping = 2 if grouping == '2x2' else 4
x_dim = 480*8 // grouping
y_dim = 512 // grouping
number_of_runs = len(runs)
data_array = np.empty((number_of_runs, x_dim, y_dim), dtype=np.float64)
s1_array = []
duration_array = []
run_number_array = []
monitor_count_array = []
progress = Progress(self, 0.0, 1.0, number_of_runs+3)
for n, run in enumerate(runs):
progress.report('Loading: '+run)
with h5py.File(run, 'r') as f:
bc = np.zeros((512*480*8),dtype=np.int64)
for b in range(8):
bc += np.bincount(f['/entry/bank'+str(b+1)+'_events/event_id'].value,minlength=512*480*8)
bc = bc.reshape((480*8, 512))
if grouping == 2:
bc = bc[::2,::2]+bc[1::2,::2]+bc[::2,1::2]+bc[1::2,1::2]
elif grouping == 4:
bc = (bc[::4,::4] + bc[1::4,::4] + bc[2::4,::4] + bc[3::4,::4]
+ bc[::4,1::4] + bc[1::4,1::4] + bc[2::4,1::4] + bc[3::4,1::4]
+ bc[::4,2::4] + bc[1::4,2::4] + bc[2::4,2::4] + bc[3::4,2::4]
+ bc[::4,3::4] + bc[1::4,3::4] + bc[2::4,3::4] + bc[3::4,3::4])
data_array[n] = bc
s1_array.append(f['/entry/DASlogs/HB2C:Mot:s1.RBV/average_value'].value[0])
duration_array.append(float(f['/entry/duration'].value[0]))
run_number_array.append(float(f['/entry/run_number'].value[0]))
monitor_count_array.append(float(f['/entry/monitor1/total_counts'].value[0]))
progress.report('Creating MDHistoWorkspace')
createWS_alg = self.createChildAlgorithm("CreateMDHistoWorkspace", enableLogging=False)
createWS_alg.setProperty("SignalInput", data_array)
createWS_alg.setProperty("ErrorInput", np.sqrt(data_array))
createWS_alg.setProperty("Dimensionality", 3)
createWS_alg.setProperty("Extents", '0.5,{},0.5,{},0.5,{}'.format(y_dim+0.5, x_dim+0.5, number_of_runs+0.5))
createWS_alg.setProperty("NumberOfBins", '{},{},{}'.format(y_dim,x_dim,number_of_runs))
createWS_alg.setProperty("Names", 'y,x,scanIndex')
createWS_alg.setProperty("Units", 'bin,bin,number')
createWS_alg.execute()
outWS = createWS_alg.getProperty("OutputWorkspace").value
progress.report('Getting IDF')
# Get the instrument and some logs from the first file; assume the rest are the same
_tmp_ws = LoadEventNexus(runs[0], MetaDataOnly=True, EnableLogging=False)
# The following logs should be the same for all runs
RemoveLogs(_tmp_ws,
KeepLogs='HB2C:Mot:detz,HB2C:Mot:detz.RBV,HB2C:Mot:s2,HB2C:Mot:s2.RBV,'
'HB2C:Mot:sgl,HB2C:Mot:sgl.RBV,HB2C:Mot:sgu,HB2C:Mot:sgu.RBV,'
'run_title,start_time,experiment_identifier,HB2C:CS:CrystalAlign:UBMatrix',
EnableLogging=False)
try:
ub = np.array(re.findall(r'-?\d+\.*\d*', _tmp_ws.run().getProperty('HB2C:CS:CrystalAlign:UBMatrix').value[0]),
dtype=np.float).reshape(3,3)
sgl = np.deg2rad(_tmp_ws.run().getProperty('HB2C:Mot:sgl.RBV').value[0]) # 'HB2C:Mot:sgl.RBV,1,0,0,-1'
sgu = np.deg2rad(_tmp_ws.run().getProperty('HB2C:Mot:sgu.RBV').value[0]) # 'HB2C:Mot:sgu.RBV,0,0,1,-1'
sgl_a = np.array([[ 1, 0, 0],
[ 0, np.cos(sgl), np.sin(sgl)],
[ 0, -np.sin(sgl), np.cos(sgl)]])
sgu_a = np.array([[ np.cos(sgu), np.sin(sgu), 0],
[-np.sin(sgu), np.cos(sgu), 0],
[ 0, 0, 1]])
UB = sgl_a.dot(sgu_a).dot(ub) # Apply the Goniometer tilts to the UB matrix
SetUB(_tmp_ws, UB=UB, EnableLogging=False)
except (RuntimeError, ValueError):
SetUB(_tmp_ws, EnableLogging=False)
if grouping > 1:
_tmp_group, _, _ = CreateGroupingWorkspace(InputWorkspace=_tmp_ws, EnableLogging=False)
group_number = 0
for x in range(0,480*8,grouping):
for y in range(0,512,grouping):
group_number += 1
for j in range(grouping):
for i in range(grouping):
_tmp_group.dataY(y+i+(x+j)*512)[0] = group_number
_tmp_ws = GroupDetectors(InputWorkspace=_tmp_ws, CopyGroupingFromWorkspace=_tmp_group, EnableLogging=False)
DeleteWorkspace(_tmp_group, EnableLogging=False)
progress.report('Adding logs')
# Hack: ConvertToMD is needed so that a deep copy of the ExperimentInfo can happen
# outWS.addExperimentInfo(_tmp_ws) # This doesn't work but should, when you delete `ws` `outWS` also loses it's ExperimentInfo
_tmp_ws = Rebin(_tmp_ws, '0,1,2', EnableLogging=False)
_tmp_ws = ConvertToMD(_tmp_ws, dEAnalysisMode='Elastic', EnableLogging=False, PreprocDetectorsWS='__PreprocessedDetectorsWS')
preprocWS = mtd['__PreprocessedDetectorsWS']
twotheta = preprocWS.column(2)
azimuthal = preprocWS.column(3)
outWS.copyExperimentInfos(_tmp_ws)
DeleteWorkspace(_tmp_ws, EnableLogging=False)
DeleteWorkspace('__PreprocessedDetectorsWS', EnableLogging=False)
# end Hack
outWS.getExperimentInfo(0).run().addProperty('s1', s1_array, True)
outWS.getExperimentInfo(0).run().getProperty('s1').units = 'deg'
outWS.getExperimentInfo(0).run().addProperty('duration', duration_array, True)
outWS.getExperimentInfo(0).run().getProperty('duration').units = 'second'
outWS.getExperimentInfo(0).run().addProperty('run_number', run_number_array, True)
outWS.getExperimentInfo(0).run().addProperty('monitor_count', monitor_count_array, True)
outWS.getExperimentInfo(0).run().addProperty('twotheta', twotheta, True)
outWS.getExperimentInfo(0).run().addProperty('azimuthal', azimuthal, True)
self.setProperty("OutputWorkspace", outWS)
def load_and_group(self, runs: List[str]) -> IMDHistoWorkspace:
"""
Load the data with given grouping
"""
# grouping config
grouping = self.getProperty("Grouping").value
if grouping == 'None':
grouping = 1
else:
grouping = 2 if grouping == '2x2' else 4
number_of_runs = len(runs)
x_dim = 480 * 8 // grouping
y_dim = 512 // grouping
data_array = np.empty((number_of_runs, x_dim, y_dim), dtype=np.float64)
s1_array = []
duration_array = []
run_number_array = []
monitor_count_array = []
progress = Progress(self, 0.0, 1.0, number_of_runs + 3)
for n, run in enumerate(runs):
progress.report('Loading: ' + run)
with h5py.File(run, 'r') as f:
bc = np.zeros((512 * 480 * 8), dtype=np.int64)
for b in range(8):
bc += np.bincount(f['/entry/bank' + str(b + 1) +
'_events/event_id'].value,
minlength=512 * 480 * 8)
bc = bc.reshape((480 * 8, 512))
if grouping == 2:
bc = bc[::2, ::2] + bc[1::2, ::2] + bc[::2,
1::2] + bc[1::2,
1::2]
elif grouping == 4:
bc = bc[::4, ::4] + bc[1::4, ::4] + bc[2::4, ::4] + bc[3::4, ::4] + bc[::4, 1::4] + bc[1::4, 1::4] + bc[2::4, 1::4] + \
bc[3::4, 1::4] + bc[::4, 2::4] + bc[1::4, 2::4] + bc[2::4, 2::4] + bc[3::4, 2::4] + bc[::4, 3::4] + \
bc[1::4, 3::4] + bc[2::4, 3::4] + bc[3::4, 3::4]
data_array[n] = bc
s1_array.append(
f['/entry/DASlogs/HB2C:Mot:s1.RBV/average_value'].value[0])
duration_array.append(float(f['/entry/duration'].value[0]))
run_number_array.append(float(f['/entry/run_number'].value[0]))
monitor_count_array.append(
float(f['/entry/monitor1/total_counts'].value[0]))
progress.report('Creating MDHistoWorkspace')
createWS_alg = self.createChildAlgorithm("CreateMDHistoWorkspace",
enableLogging=False)
createWS_alg.setProperty("SignalInput", data_array)
createWS_alg.setProperty("ErrorInput", np.sqrt(data_array))
createWS_alg.setProperty("Dimensionality", 3)
createWS_alg.setProperty(
"Extents", '0.5,{},0.5,{},0.5,{}'.format(y_dim + 0.5, x_dim + 0.5,
number_of_runs + 0.5))
createWS_alg.setProperty(
"NumberOfBins", '{},{},{}'.format(y_dim, x_dim, number_of_runs))
createWS_alg.setProperty("Names", 'y,x,scanIndex')
createWS_alg.setProperty("Units", 'bin,bin,number')
createWS_alg.execute()
outWS = createWS_alg.getProperty("OutputWorkspace").value
progress.report('Getting IDF')
# Get the instrument and some logs from the first file; assume the rest are the same
_tmp_ws = LoadEventNexus(runs[0],
MetaDataOnly=True,
EnableLogging=False)
# The following logs should be the same for all runs
RemoveLogs(
_tmp_ws,
KeepLogs=
'HB2C:Mot:detz,HB2C:Mot:detz.RBV,HB2C:Mot:s2,HB2C:Mot:s2.RBV,'
'HB2C:Mot:sgl,HB2C:Mot:sgl.RBV,HB2C:Mot:sgu,HB2C:Mot:sgu.RBV,'
'run_title,start_time,experiment_identifier,HB2C:CS:CrystalAlign:UBMatrix',
EnableLogging=False)
time_ns_array = _tmp_ws.run().startTime().totalNanoseconds(
) + np.append(0,
np.cumsum(duration_array) * 1e9)[:-1]
try:
ub = np.array(re.findall(
r'-?\d+\.*\d*',
_tmp_ws.run().getProperty(
'HB2C:CS:CrystalAlign:UBMatrix').value[0]),
dtype=float).reshape(3, 3)
sgl = np.deg2rad(_tmp_ws.run().getProperty(
'HB2C:Mot:sgl.RBV').value[0]) # 'HB2C:Mot:sgl.RBV,1,0,0,-1'
sgu = np.deg2rad(_tmp_ws.run().getProperty(
'HB2C:Mot:sgu.RBV').value[0]) # 'HB2C:Mot:sgu.RBV,0,0,1,-1'
sgl_a = np.array([[1, 0, 0], [0, np.cos(sgl),
np.sin(sgl)],
[0, -np.sin(sgl), np.cos(sgl)]])
sgu_a = np.array([[np.cos(sgu), np.sin(sgu), 0],
[-np.sin(sgu), np.cos(sgu), 0], [0, 0, 1]])
UB = sgl_a.dot(sgu_a).dot(
ub) # Apply the Goniometer tilts to the UB matrix
SetUB(_tmp_ws, UB=UB, EnableLogging=False)
except (RuntimeError, ValueError):
SetUB(_tmp_ws, EnableLogging=False)
if grouping > 1:
_tmp_group, _, _ = CreateGroupingWorkspace(InputWorkspace=_tmp_ws,
EnableLogging=False)
group_number = 0
for x in range(0, 480 * 8, grouping):
for y in range(0, 512, grouping):
group_number += 1
for j in range(grouping):
for i in range(grouping):
_tmp_group.dataY(y + i +
(x + j) * 512)[0] = group_number
_tmp_ws = GroupDetectors(InputWorkspace=_tmp_ws,
CopyGroupingFromWorkspace=_tmp_group,
EnableLogging=False)
DeleteWorkspace(_tmp_group, EnableLogging=False)
progress.report('Adding logs')
# Hack: ConvertToMD is needed so that a deep copy of the ExperimentInfo can happen
# outWS.addExperimentInfo(_tmp_ws) # This doesn't work but should, when you delete `ws` `outWS` also loses it's ExperimentInfo
_tmp_ws = Rebin(_tmp_ws, '0,1,2', EnableLogging=False)
_tmp_ws = ConvertToMD(_tmp_ws,
dEAnalysisMode='Elastic',
EnableLogging=False,
PreprocDetectorsWS='__PreprocessedDetectorsWS')
preprocWS = mtd['__PreprocessedDetectorsWS']
twotheta = preprocWS.column(2)
azimuthal = preprocWS.column(3)
outWS.copyExperimentInfos(_tmp_ws)
DeleteWorkspace(_tmp_ws, EnableLogging=False)
DeleteWorkspace('__PreprocessedDetectorsWS', EnableLogging=False)
# end Hack
add_time_series_property('s1',
outWS.getExperimentInfo(0).run(),
time_ns_array, s1_array)
outWS.getExperimentInfo(0).run().getProperty('s1').units = 'deg'
add_time_series_property('duration',
outWS.getExperimentInfo(0).run(),
time_ns_array, duration_array)
outWS.getExperimentInfo(0).run().getProperty(
'duration').units = 'second'
outWS.getExperimentInfo(0).run().addProperty('run_number',
run_number_array, True)
add_time_series_property('monitor_count',
outWS.getExperimentInfo(0).run(),
time_ns_array, monitor_count_array)
outWS.getExperimentInfo(0).run().addProperty('twotheta', twotheta,
True)
outWS.getExperimentInfo(0).run().addProperty('azimuthal', azimuthal,
True)
setGoniometer_alg = self.createChildAlgorithm("SetGoniometer",
enableLogging=False)
setGoniometer_alg.setProperty("Workspace", outWS)
setGoniometer_alg.setProperty("Axis0", 's1,0,1,0,1')
setGoniometer_alg.setProperty("Average", False)
setGoniometer_alg.execute()
return outWS