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SNSPowderReduction.py
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SNSPowderReduction.py
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# Mantid Repository : https://github.com/mantidproject/mantid
#
# Copyright © 2018 ISIS Rutherford Appleton Laboratory UKRI,
# NScD Oak Ridge National Laboratory, European Spallation Source,
# Institut Laue - Langevin & CSNS, Institute of High Energy Physics, CAS
# SPDX - License - Identifier: GPL - 3.0 +
#pylint: disable=invalid-name,no-init,too-many-lines
import os
from pathlib import Path
import mantid.simpleapi as api
from mantid.api import mtd, AlgorithmFactory, AnalysisDataService, DistributedDataProcessorAlgorithm, \
FileAction, FileProperty, ITableWorkspaceProperty, MultipleFileProperty, PropertyMode, WorkspaceProperty, \
ITableWorkspace, MatrixWorkspace
from mantid.kernel import (
ConfigService, Direction, EnabledWhenProperty, FloatArrayProperty, FloatBoundedValidator, IntArrayBoundedValidator,
IntArrayProperty, MaterialBuilder, Property, PropertyCriterion, PropertyManagerDataService, StringListValidator,
StringTimeSeriesProperty)
from mantid.dataobjects import SplittersWorkspace # SplittersWorkspace
from mantid.utils import absorptioncorrutils
if AlgorithmFactory.exists('GatherWorkspaces'):
HAVE_MPI = True
from mpi4py import MPI
mpiRank = MPI.COMM_WORLD.Get_rank()
else:
HAVE_MPI = False
mpiRank = 0 # simplify if clauses
EVENT_WORKSPACE_ID = "EventWorkspace"
EXTENSIONS_NXS = ["_event.nxs", ".nxs.h5"]
def noRunSpecified(runs):
""" Check whether any run is specified
Purpose:
Check whether any run, which is larger than 0, is specified in the input numpy array
Requirements:
Input is a numpy array
Guarantees:
A boolean is returned
:param runs:
:return:
"""
# return True if runs is of size zero
if len(runs) <= 0:
return True
# return True if there is one and only one run in runs and it is not greater than 0.
if len(runs) == 1:
# break off the instrument part
value = int(runs[0].split('_')[-1])
# -1 turns off the runnumber
return value <= 0
return False
def get_workspace(workspace_name):
"""
Purpose: Get the reference of a workspace
Requirements:
1. workspace_name is a string
Guarantees:
The reference of the workspace with same is returned.
:exception RuntimeError: a RuntimeError from Mantid will be thrown
:param workspace_name:
:return:
"""
assert isinstance(workspace_name, str), 'Input workspace name {0} must be a string but not a {1}.' \
''.format(workspace_name, type(workspace_name))
return AnalysisDataService.retrieve(workspace_name)
def is_event_workspace(workspace):
if isinstance(workspace, str):
return get_workspace(workspace).id() == EVENT_WORKSPACE_ID
else:
return workspace.id() == EVENT_WORKSPACE_ID
def allEventWorkspaces(*args):
"""
Purpose:
Check whether all the inputs are event workspace
Requirements:
Each element in the args is a string as name of workspace
Guarantees:
return boolean
@param args:
@return:
"""
result = True
args = [is_event_workspace(arg) for arg in args]
for arg in args:
result = result and arg
return result
def getBasename(filename):
if type(filename) == list:
filename = filename[0]
name = os.path.split(filename)[-1]
for extension in EXTENSIONS_NXS:
name = name.replace(extension, '')
return name
#pylint: disable=too-many-instance-attributes
class SNSPowderReduction(DistributedDataProcessorAlgorithm):
COMPRESS_TOL_TOF = .01
_resampleX = None
_binning = None
_bin_in_dspace = None
_instrument = None
_filterBadPulses = None
_removePromptPulseWidth = None
_LRef = None
_DIFCref = None
_wavelengthMin = None
_wavelengthMax = None
_vanPeakFWHM = None
_vanSmoothing = None
_vanRadius = None
_scaleFactor = None
_outDir = None
_outPrefix = None
_outTypes = None
_chunks = None
_splittersWS = None
_splitinfotablews = None
_normalisebycurrent = None
_lowResTOFoffset = None
_focusPos = None
_charTable = None
iparmFile = None
_info = None
_absMethod = None
_sampleFormula = None
_massDensity = None
_containerShape = None
def category(self):
return "Diffraction\\Reduction"
def seeAlso(self):
return [ "DiffractionFocussing","AlignAndFocusPowder" ]
def name(self):
return "SNSPowderReduction"
def summary(self):
"""
summary of the algorithm
:return:
"""
return "The algorithm used for reduction of powder diffraction data obtained on SNS instruments (e.g. PG3) "
def PyInit(self):
self.copyProperties('AlignAndFocusPowderFromFiles', ['Filename', 'PreserveEvents', 'DMin', 'DMax', 'DeltaRagged'])
self.declareProperty("Sum", False,
"Sum the runs. Does nothing for characterization runs")
self.declareProperty("PushDataPositive", "None",
StringListValidator(["None", "ResetToZero", "AddMinimum"]),
"Add a constant to the data that makes it positive over the whole range.")
arrvalidator = IntArrayBoundedValidator(lower=-1)
self.declareProperty(IntArrayProperty("BackgroundNumber", values=[0], validator=arrvalidator),
doc="If specified overrides value in CharacterizationRunsFile If -1 turns off correction.")
self.declareProperty(IntArrayProperty("VanadiumNumber", values=[0], validator=arrvalidator),
doc="If specified overrides value in CharacterizationRunsFile. If -1 turns off correction.")
self.declareProperty(IntArrayProperty("VanadiumBackgroundNumber", values=[0], validator=arrvalidator),
doc="If specified overrides value in CharacterizationRunsFile. If -1 turns off correction.")
self.declareProperty(FileProperty(name="CalibrationFile",defaultValue="",action=FileAction.OptionalLoad,
extensions=[".h5", ".hd5", ".hdf", ".cal"])) # CalFileName
self.declareProperty(FileProperty(name="GroupingFile",defaultValue="",action=FileAction.OptionalLoad,
extensions=[".xml"]), "Overrides grouping from CalibrationFile")
self.declareProperty(MultipleFileProperty(name="CharacterizationRunsFile",
action=FileAction.OptionalLoad,
extensions=["txt"]), "File with characterization runs denoted")
self.declareProperty(FileProperty(name="ExpIniFilename", defaultValue="", action=FileAction.OptionalLoad,
extensions=[".ini"]))
self.copyProperties('AlignAndFocusPowderFromFiles',
['LorentzCorrection', 'UnwrapRef', 'LowResRef', 'CropWavelengthMin', 'CropWavelengthMax',
'RemovePromptPulseWidth', 'MaxChunkSize'])
self.declareProperty(FloatArrayProperty("Binning", values=[0., 0., 0.],
direction=Direction.Input),
"Positive is linear bins, negative is logorithmic") # Params
self.copyProperties('AlignAndFocusPowderFromFiles', ['ResampleX'])
self.declareProperty("BinInDspace", True,
"If all three bin parameters a specified, whether they are in dspace (true) or "
"time-of-flight (false)") # DSpacing
# section of vanadium run processing
self.declareProperty("StripVanadiumPeaks", True,
"Subtract fitted vanadium peaks from the known positions.")
self.declareProperty("VanadiumFWHM", 7, "Default=7")
self.declareProperty("VanadiumPeakTol", 0.05,
"How far from the ideal position a vanadium peak can be during StripVanadiumPeaks. "
"Default=0.05, negative turns off")
self.declareProperty("VanadiumSmoothParams", "20,2", "Default=20,2")
self.declareProperty("VanadiumRadius", .3175, "Radius for CarpenterSampleCorrection")
self.declareProperty("BackgroundSmoothParams", "", "Default=off, suggested 20,2")
# filtering
self.declareProperty("FilterBadPulses", 95., # different default value
doc="Filter out events measured while proton charge is more than 5% below average")
self.declareProperty("ScaleData", defaultValue=1., validator=FloatBoundedValidator(lower=0., exclusive=True),
doc="Constant to multiply the data before writing out. This does not apply to "
"PDFgetN files.")
self.declareProperty("OffsetData", defaultValue=0., validator=FloatBoundedValidator(lower=0., exclusive=False),
doc="Constant to add to the data before writing out. This does not apply to "
"PDFgetN files.")
self.declareProperty("SaveAs", "gsas",
"List of all output file types. Allowed values are 'fullprof', 'gsas', 'nexus', "
"'pdfgetn', and 'topas'")
self.declareProperty("OutputFilePrefix", "", "Overrides the default filename for the output file (Optional).")
self.declareProperty(FileProperty(name="OutputDirectory", defaultValue="",action=FileAction.Directory))
# Caching options
self.declareProperty( 'CacheDir', "", 'comma-delimited ascii string representation of a list of candidate cache directories')
self.declareProperty('CleanCache', False, 'Remove all cache files within CacheDir')
self.setPropertySettings('CleanCache', EnabledWhenProperty('CacheDir', PropertyCriterion.IsNotDefault))
property_names = ('CacheDir', 'CleanCache')
[self.setPropertyGroup(name, 'Caching') for name in property_names]
self.declareProperty("FinalDataUnits", "dSpacing", StringListValidator(["dSpacing","MomentumTransfer"]))
# absorption correction
self.declareProperty("TypeOfCorrection", "None",
StringListValidator(["None", "SampleOnly", "SampleAndContainer", "FullPaalmanPings"]),
doc="Specifies the Absorption Correction terms to calculate, if any.")
self.declareProperty("SampleFormula", "", doc="Chemical formula of the sample")
self.declareProperty("SampleGeometry", {}, doc="A dictionary of geometry parameters for the sample.")
self.declareProperty("MeasuredMassDensity", defaultValue=0.1,
validator=FloatBoundedValidator(lower=0., exclusive=True),
doc="Measured mass density of sample in g/cc") # in g/cc, way to validate?
self.declareProperty("SampleNumberDensity", defaultValue=Property.EMPTY_DBL,
doc="Number density of the sample in number of atoms per cubic Angstrom will be used instead of calculated")
self.declareProperty("ContainerShape", defaultValue="PAC06", doc="Defines the container geometry")
self.declareProperty("ContainerScaleFactor", defaultValue=1.0,
validator=FloatBoundedValidator(lower=0),
doc="Factor to scale the container data")
self.copyProperties("AbsorptionCorrection", "ElementSize")
self.declareProperty("NumWavelengthBins", defaultValue=1000,
doc="Number of wavelength bin to calculate the for absorption correction")
workspace_prop = WorkspaceProperty('SplittersWorkspace', '', Direction.Input, PropertyMode.Optional)
self.declareProperty(workspace_prop, "Splitters workspace for split event workspace.")
# replaced for matrix workspace, SplittersWorkspace and table workspace
# tableprop = ITableWorkspaceProperty("SplittersWorkspace", "", Direction.Input, PropertyMode.Optional)
# self.declareProperty(tableprop, "Splitters workspace for split event workspace.")
infotableprop = ITableWorkspaceProperty("SplitInformationWorkspace", "", Direction.Input, PropertyMode.Optional)
self.declareProperty(infotableprop, "Name of table workspace containing information for splitters.")
self.declareProperty("LowResolutionSpectraOffset", -1,
"If larger and equal to 0, then process low resolution TOF and offset is the spectra "
"number. Otherwise, ignored.") # LowResolutionSpectraOffset
self.declareProperty("NormalizeByCurrent", True, "Normalize by current")
self.declareProperty("CompressTOFTolerance", 0.01, "Tolerance to compress events in TOF.")
self.copyProperties('AlignAndFocusPowderFromFiles', ['FrequencyLogNames', 'WaveLengthLogNames'])
return
def validateInputs(self):
issues = dict()
# If doing absorption correction, make sure the sample formula is correct
if self.getProperty("TypeOfCorrection").value != "None":
if self.getProperty("SampleFormula").value.strip() != '':
try:
MaterialBuilder().setFormula(self.getProperty("SampleFormula").value.strip())
except ValueError as ex:
issues['SampleFormula'] = "Invalid SampleFormula: '{}'".format(str(ex))
# The provided cache directory does not exist
cache_dir_string = self.getProperty('CacheDir').value # comma-delimited string representation of list
if bool(cache_dir_string):
cache_dirs = [candidate.strip() for candidate in cache_dir_string.split(',')]
for cache_dir in cache_dirs:
if bool(cache_dir) and Path(cache_dir).exists() is False:
issues['CacheDir'] = f'Directory {cache_dir} does not exist'
# We cannot clear the cache if property "CacheDir" has not been set
if self.getProperty('CleanCache').value and not bool(self.getProperty('CacheDir').value):
issues['CleanCache'] = 'Property "CacheDir" must be set in order to clean the cache'
return issues
#pylint: disable=too-many-locals,too-many-branches,too-many-statements
def PyExec(self): # noqa
""" Main execution body
"""
# get generic information
self._loadCharacterizations()
self._resampleX = self.getProperty("ResampleX").value
if self._resampleX != 0.:
self._binning = [0.]
else:
self._binning = self.getProperty("Binning").value
if len(self._binning) != 1 and len(self._binning) != 3:
raise RuntimeError("Can only specify (width) or (start,width,stop) for binning. Found %d values." % len(self._binning))
if len(self._binning) == 3:
if self._binning[0] == 0. and self._binning[1] == 0. and self._binning[2] == 0.:
raise RuntimeError("Failed to specify the binning")
self._bin_in_dspace = self.getProperty("BinInDspace").value
self._filterBadPulses = self.getProperty("FilterBadPulses").value
self._removePromptPulseWidth = self.getProperty("RemovePromptPulseWidth").value
self._lorentz = self.getProperty("LorentzCorrection").value
self._LRef = self.getProperty("UnwrapRef").value
self._DIFCref = self.getProperty("LowResRef").value
self._wavelengthMin = self.getProperty("CropWavelengthMin").value
self._wavelengthMax = self.getProperty("CropWavelengthMax").value
self._vanPeakFWHM = self.getProperty("VanadiumFWHM").value
self._vanSmoothing = self.getProperty("VanadiumSmoothParams").value
self._vanRadius = self.getProperty("VanadiumRadius").value
self.calib = self.getProperty("CalibrationFile").value
self._scaleFactor = self.getProperty("ScaleData").value
self._offsetFactor = self.getProperty("OffsetData").value
self._outDir = self.getProperty("OutputDirectory").value
# Caching options
self._cache_dirs = [os.path.abspath(me.strip()) for me in self.getProperty("CacheDir").value.split(',')
if me.strip()] # filter out empty elements
self._cache_dir = self._cache_dirs[0] if self._cache_dirs else ""
self._clean_cache = self.getProperty("CleanCache").value
self._outPrefix = self.getProperty("OutputFilePrefix").value.strip()
self._outTypes = self.getProperty("SaveAs").value.lower()
self._absMethod = self.getProperty("TypeOfCorrection").value
self._sampleFormula = self.getProperty("SampleFormula").value
self._sampleGeometry = self.getProperty("SampleGeometry").value
self._massDensity = self.getProperty("MeasuredMassDensity").value
self._numberDensity = self.getProperty("SampleNumberDensity").value
self._containerShape = self.getProperty("ContainerShape").value
self._containerScaleFactor = self.getProperty("ContainerScaleFactor").value
self._elementSize = self.getProperty("ElementSize").value
self._num_wl_bins = self.getProperty("NumWavelengthBins").value
samRuns = self._getLinearizedFilenames("Filename")
self._determineInstrument(samRuns[0])
preserveEvents = self.getProperty("PreserveEvents").value
if HAVE_MPI and preserveEvents:
self.log().warning("preserveEvents set to False for MPI tasks.")
preserveEvents = False
self._info = None
self._chunks = self.getProperty("MaxChunkSize").value
# define splitters workspace and filter wall time
self._splittersWS = self.getProperty("SplittersWorkspace").value
if self._splittersWS is not None:
# user specifies splitters workspace
self.log().information("SplittersWorkspace is %s" % (str(self._splittersWS)))
if len(samRuns) != 1:
# TODO/FUTURE - This should be supported
raise RuntimeError("Reducing data with splitters cannot happen when there are more than 1 sample run.")
# define range of wall-time to import data
sample_time_filter_wall = self._get_time_filter_wall(self._splittersWS.name(), samRuns[0])
self.log().information("The time filter wall is %s" %(str(sample_time_filter_wall)))
else:
sample_time_filter_wall = (0.0, 0.0)
self.log().information("SplittersWorkspace is None, and thus there is NO time filter wall. ")
self._splitinfotablews = self.getProperty("SplitInformationWorkspace").value
self._normalisebycurrent = self.getProperty("NormalizeByCurrent").value
# Tolerance for compress TOF event. If given a negative value, then use default 0.01
self.COMPRESS_TOL_TOF = float(self.getProperty("CompressTOFTolerance").value)
if self.COMPRESS_TOL_TOF < -0.:
self.COMPRESS_TOL_TOF = 0.01
# Clean the cache directory if so requested
if self._clean_cache:
api.CleanFileCache(CacheDir=self._cache_dir, AgeInDays=0)
# Process data
# List stores the workspacename of all data workspaces that will be converted to d-spacing in the end.
workspacelist = []
samwksplist = []
self._lowResTOFoffset = self.getProperty("LowResolutionSpectraOffset").value
focuspos = self._focusPos
if self._lowResTOFoffset >= 0:
# Dealing with the parameters for editing instrument parameters
if "PrimaryFlightPath" in focuspos:
l1 = focuspos["PrimaryFlightPath"]
if l1 > 0:
specids = focuspos['SpectrumIDs'][:]
l2s = focuspos['L2'][:]
polars = focuspos['Polar'][:]
phis = focuspos['Azimuthal'][:]
specids.extend(specids)
l2s.extend(l2s)
polars.extend(polars)
phis.extend(phis)
focuspos['SpectrumIDs'] = specids
focuspos['L2'] = l2s
focuspos['Polar'] = polars
focuspos['Azimuthal'] = phis
# ENDIF
# calculate absorption from first sample run
metaws = None
if self._absMethod != "None" and self._info is None:
absName = '__{}_abs'.format(getBasename(samRuns[0]))
api.Load(Filename=samRuns[0], OutputWorkspace=absName, MetaDataOnly=True)
self._info = self._getinfo(absName)
metaws = absName
if self._sampleFormula == '' and "SampleFormula" in mtd[metaws].run():
# Do a quick check to see if the sample formula in the logs is correct
try:
MaterialBuilder().setFormula(mtd[metaws].run()["SampleFormula"].lastValue().strip())
except ValueError:
self.log().warning(
"Sample formula '{}' found in sample logs does not have a valid format - specify manually in "
"algorithm input.".format(mtd[metaws].run()["SampleFormula"].lastValue().strip()))
# NOTE: inconsistent naming among different methods
# -> adding more comments to help clarify
a_sample, a_container = absorptioncorrutils.calculate_absorption_correction(
samRuns[0], # filename: File to be used for absorption correction
self._absMethod, # [None, SampleOnly, SampleAndContainer, FullPaalmanPings]
self._info, # PropertyManager of run characterizations
self._sampleFormula, # Material for absorption correction
self._massDensity, # Mass density of the sample
self._sampleGeometry, # Geometry parameters for the sample
self._numberDensity, # Optional number density of sample to be added
self._containerShape, # Shape definition of container
self._num_wl_bins, # Number of bins: len(ws.readX(0))-1
self._elementSize, # Size of one side of the integration element cube in mm
metaws, # Optional workspace containing metadata
self._cache_dirs, # Cache dir for absorption correction workspace
)
if self.getProperty("Sum").value and len(samRuns) > 1:
self.log().information('Ignoring value of "Sum" property')
# Sum input sample runs and then do reduction
if self._splittersWS is not None:
raise NotImplementedError("Summing spectra and filtering events are not supported simultaneously.")
sam_ws_name = self._focusAndSum(samRuns, preserveEvents=preserveEvents, absorptionWksp=a_sample)
assert isinstance(sam_ws_name, str), 'Returned from _focusAndSum() must be a string but not' \
'%s. ' % str(type(sam_ws_name))
workspacelist.append(sam_ws_name)
samwksplist.append(sam_ws_name)
# ENDIF (SUM)
else:
# Process each sample run
for sam_run_number in samRuns:
# first round of processing the sample
self._info = None
if sample_time_filter_wall[0] == 0. and sample_time_filter_wall[-1] == 0. \
and self._splittersWS is None:
returned = self._focusAndSum([sam_run_number], preserveEvents=preserveEvents, absorptionWksp=a_sample)
else:
returned = self._focusChunks(sam_run_number, sample_time_filter_wall,
splitwksp=self._splittersWS,
preserveEvents=preserveEvents)
if isinstance(returned, list):
# Returned with a list of workspaces
focusedwksplist = returned
for sam_ws_name in focusedwksplist:
assert isinstance(sam_ws_name, str), 'Impossible to have a non-string value in ' \
'returned focused workspaces\' names.'
samwksplist.append(sam_ws_name)
workspacelist.append(sam_ws_name)
# END-FOR
else:
# returned as a single workspace
sam_ws_name = returned
assert isinstance(sam_ws_name, str)
samwksplist.append(sam_ws_name)
workspacelist.append(sam_ws_name)
# ENDIF
# END-FOR
# ENDIF (Sum data or not)
for (samRunIndex, sam_ws_name) in enumerate(samwksplist):
assert isinstance(sam_ws_name, str), 'Assuming that samRun is a string. But it is %s' % str(type(sam_ws_name))
if is_event_workspace(sam_ws_name):
self.log().information('Sample Run %s: starting number of events = %d.' % (
sam_ws_name, get_workspace(sam_ws_name).getNumberEvents()))
# Get run information
self._info = self._getinfo(sam_ws_name)
# process the container
can_run_numbers = self._info["container"].value
can_run_numbers = ['%s_%d' % (self._instrument, value) for value in can_run_numbers]
# Check if existing container
# - has history and is using SNSPowderReduction
# - was created using the same method
# -> carry on as usual
# - was created with a different method
# -> delete the current one, then carry on
# - no history (processing list of runs)
# -> carry on as a single call wiht list of runs ensures that same method is used.
can_run_ws_name, _ = self._generate_container_run_name(can_run_numbers, samRunIndex)
if can_run_ws_name in mtd:
hstry = mtd[can_run_ws_name].getHistory()
if not hstry.empty():
alg = hstry.getAlgorithmHistory(0)
if alg.name() == "SNSPowderReduction":
if alg.getPropertyValue("TypeOfCorrection") != self._absMethod:
self.log().information(
f"Remove {can_run_ws_name} as it is generated with a different method"
)
mtd.remove(can_run_ws_name)
can_run_ws_name = self._process_container_runs(can_run_numbers,
samRunIndex,
preserveEvents,
absorptionWksp=a_container)
if can_run_ws_name is not None:
workspacelist.append(can_run_ws_name)
# process the vanadium run
van_run_number_list = self._info["vanadium"].value
van_run_number_list = ['%s_%d' % (self._instrument, value) for value in van_run_number_list]
van_specified = not noRunSpecified(van_run_number_list)
if van_specified:
van_run_ws_name = self._process_vanadium_runs(van_run_number_list, samRunIndex)
workspacelist.append(van_run_ws_name)
else:
van_run_ws_name = None
# return if MPI is used and there is more than 1 processor
if mpiRank > 0:
return
# return if there is no sample run
# Note: sample run must exist in logic
# VZ: Discuss with Pete
# if sam_ws_name == 0:
# return
# the final bit of math to remove container run and vanadium run
if can_run_ws_name is not None and self._containerScaleFactor != 0:
# must convert the sample to a matrix workspace if the can run isn't one
if not allEventWorkspaces(can_run_ws_name, sam_ws_name):
api.ConvertToMatrixWorkspace(InputWorkspace=sam_ws_name,
OutputWorkspace=sam_ws_name)
# remove container run
api.RebinToWorkspace(WorkspaceToRebin=can_run_ws_name,
WorkspaceToMatch=sam_ws_name,
OutputWorkspace=can_run_ws_name)
api.Scale(InputWorkspace=can_run_ws_name,
OutputWorkspace=can_run_ws_name,
Factor=self._containerScaleFactor)
api.Minus(LHSWorkspace=sam_ws_name,
RHSWorkspace=can_run_ws_name,
OutputWorkspace=sam_ws_name)
# compress event if the sample run workspace is EventWorkspace
if is_event_workspace(sam_ws_name) and self.COMPRESS_TOL_TOF > 0.:
api.CompressEvents(InputWorkspace=sam_ws_name,
OutputWorkspace=sam_ws_name,
Tolerance=self.COMPRESS_TOL_TOF) # 10ns
# canRun = str(canRun)
if van_run_ws_name is not None:
# subtract vanadium run from sample run by division
num_hist_sam = get_workspace(sam_ws_name).getNumberHistograms()
num_hist_van = get_workspace(van_run_ws_name).getNumberHistograms()
assert num_hist_sam == num_hist_van, \
'Number of histograms of sample %d is not equal to van %d.' % (num_hist_sam, num_hist_van)
api.Divide(LHSWorkspace=sam_ws_name,
RHSWorkspace=van_run_ws_name,
OutputWorkspace=sam_ws_name)
normalized = True
van_run_ws = get_workspace(van_run_ws_name)
sam_ws = get_workspace(sam_ws_name)
sam_ws.getRun()['van_number'] = van_run_ws.getRun()['run_number'].value
# van_run_number = str(van_run_number)
else:
normalized = False
# Compress the event again
if is_event_workspace(sam_ws_name) and self.COMPRESS_TOL_TOF > 0.:
api.CompressEvents(InputWorkspace=sam_ws_name,
OutputWorkspace=sam_ws_name,
Tolerance=self.COMPRESS_TOL_TOF) # 5ns/
# write out the files
# FIXME - need documentation for mpiRank
if mpiRank == 0:
if self._scaleFactor != 1.:
api.Scale(sam_ws_name, Factor=self._scaleFactor, OutputWorkspace=sam_ws_name)
if self._offsetFactor != 0.:
api.ConvertToMatrixWorkspace(InputWorkspace=sam_ws_name,
OutputWorkspace=sam_ws_name)
api.Scale(sam_ws_name, Factor=self._offsetFactor, OutputWorkspace=sam_ws_name,
Operation='Add')
# make sure there are no negative values - gsas hates them
if self.getProperty("PushDataPositive").value != "None":
addMin = self.getProperty("PushDataPositive").value == "AddMinimum"
api.ResetNegatives(InputWorkspace=sam_ws_name,
OutputWorkspace=sam_ws_name,
AddMinimum=addMin,
ResetValue=0.)
self._save(sam_ws_name, self._info, normalized, False)
# ENDFOR
# convert everything into d-spacing as the final units
if mpiRank == 0:
workspacelist = set(workspacelist) # only do each workspace once
for wksp in workspacelist:
api.ConvertUnits(InputWorkspace=wksp, OutputWorkspace=wksp,
Target=self.getProperty("FinalDataUnits").value)
propertyName = "OutputWorkspace%s" % wksp
if not self.existsProperty(propertyName):
self.declareProperty(WorkspaceProperty(propertyName, wksp, Direction.Output))
self.setProperty(propertyName, wksp)
return
def _getLinearizedFilenames(self, propertyName):
runnumbers = self.getProperty(propertyName).value
linearizedRuns = []
for item in runnumbers:
if type(item) == list:
linearizedRuns.extend(item)
else:
linearizedRuns.append(item)
return linearizedRuns
def _determineInstrument(self, filename):
name = getBasename(filename)
parts = name.split('_')
self._instrument = ConfigService.getInstrument(parts[0]).shortName() # only works for instruments without '_'
def _loadCharacterizations(self):
self._focusPos = {}
charFilename = self.getProperty("CharacterizationRunsFile").value
charFilename = ','.join(charFilename)
expIniFilename = self.getProperty("ExpIniFilename").value
self._charTable = ''
if charFilename is None or len(charFilename) <= 0:
self.iparmFile = None
return
self._charTable = 'characterizations'
results = api.PDLoadCharacterizations(Filename=charFilename,
ExpIniFilename=expIniFilename,
OutputWorkspace=self._charTable)
# export the characterizations table
charTable = results[0]
if not self.existsProperty("CharacterizationsTable"):
self.declareProperty(ITableWorkspaceProperty("CharacterizationsTable", self._charTable, Direction.Output))
self.setProperty("CharacterizationsTable", charTable)
# get the focus positions from the properties
self.iparmFile = results[1]
self._focusPos['PrimaryFlightPath'] = results[2]
self._focusPos['SpectrumIDs'] = results[3]
self._focusPos['L2'] = results[4]
self._focusPos['Polar'] = results[5]
self._focusPos['Azimuthal'] = results[6]
#pylint: disable=too-many-branches
def _load_event_data(self, filename, filter_wall=None, out_ws_name=None, **chunk):
""" Load data optionally by chunk strategy
Purpose:
Load a complete or partial run, filter bad pulses.
Output workspace name is formed as
- user specified (highest priority)
- instrument-name_run-number_0 (no chunking)
- instrument-name_run-number_X: X is either ChunkNumber of SpectrumMin
Requirements:
1. run number is integer and larger than 0
Guarantees:
A workspace is created with name described above
:param filename:
:param filter_wall:
:param out_ws_name: name of output workspace specified by user. it will override the automatic name
:param chunk:
:return:
"""
# Check requirements
assert len(filename) > 0, "Input file '%s' does not exist" % filename
assert (chunk is None) or isinstance(chunk, dict), 'Input chunk must be either a dictionary or None.'
# get event file's base name
base_name = getBasename(filename)
# give out the default output workspace name
if out_ws_name is None:
if chunk:
if "ChunkNumber" in chunk:
out_ws_name = base_name + "__chk%d" % (int(chunk["ChunkNumber"]))
elif "SpectrumMin" in chunk:
seq_number = 1 + int(chunk["SpectrumMin"])/(int(chunk["SpectrumMax"])-int(chunk["SpectrumMin"]))
out_ws_name = base_name + "__chk%d" % seq_number
else:
out_ws_name = "%s_%d" % (base_name, 0)
# END-IF
# Specify the other chunk information including Precount, FilterByTimeStart and FilterByTimeStop.
if filename.endswith("_event.nxs") or filename.endswith(".nxs.h5"):
chunk["Precount"] = True
if filter_wall is not None:
if filter_wall[0] > 0.:
chunk["FilterByTimeStart"] = filter_wall[0]
if filter_wall[1] > 0.:
chunk["FilterByTimeStop"] = filter_wall[1]
# Call Mantid's Load algorithm to load complete or partial data
api.Load(Filename=filename, OutputWorkspace=out_ws_name, **chunk)
# Log output
if is_event_workspace(out_ws_name):
self.log().debug("Load run %s: number of events = %d. " % (os.path.split(filename)[-1],
get_workspace(out_ws_name).getNumberEvents()))
if HAVE_MPI:
msg = "MPI Task = %s ;" % (str(mpiRank))
if is_event_workspace(out_ws_name):
msg += "Number Events = " + str(get_workspace(out_ws_name).getNumberEvents())
self.log().debug(msg)
# filter bad pulses
if self._filterBadPulses > 0.:
# record number of events of the original workspace
if is_event_workspace(out_ws_name):
# Event workspace: record original number of events
num_original_events = get_workspace(out_ws_name).getNumberEvents()
else:
num_original_events = -1
# filter bad pulse
api.FilterBadPulses(InputWorkspace=out_ws_name, OutputWorkspace=out_ws_name,
LowerCutoff=self._filterBadPulses)
if is_event_workspace(out_ws_name):
# Event workspace
message = "FilterBadPulses reduces number of events from %d to %d (under %s percent) " \
"of workspace %s." % (num_original_events, get_workspace(out_ws_name).getNumberEvents(),
str(self._filterBadPulses), out_ws_name)
self.log().information(message)
# END-IF (filter bad pulse)
return out_ws_name
def _getStrategy(self, filename):
"""
Get chunking strategy by calling mantid algorithm 'DetermineChunking'
:param filename:
:return: a list of dictionary. Each dictionary is a row in table workspace
"""
# Determine chunk strategy can search in archive.
# Therefore this will fail: assert os.path.exists(file_name), 'NeXus file %s does not exist.' % file_name
self.log().debug("[Fx116] Run file Name : %s,\t\tMax chunk size: %s" %
(filename, str(self._chunks)))
chunks = api.DetermineChunking(Filename=filename, MaxChunkSize=self._chunks)
strategy = []
for row in chunks:
strategy.append(row)
# For table with no rows
if len(strategy) == 0:
strategy.append({})
# delete chunks workspace
chunks = str(chunks)
mtd.remove(chunks)
return strategy
def __logChunkInfo(self, chunk):
keys = sorted(chunk.keys())
keys = [str(key) + "=" + str(chunk[key]) for key in keys]
self.log().information("Working on chunk [" + ", ".join(keys) + "]")
def checkInfoMatch(self, left, right):
if (left["frequency"].value is not None) and (right["frequency"].value is not None) \
and (abs(left["frequency"].value - right["frequency"].value)/left["frequency"].value > .05):
raise RuntimeError("Cannot add incompatible frequencies (%f!=%f)"
% (left["frequency"].value, right["frequency"].value))
if (left["wavelength"].value is not None) and (right["wavelength"].value is not None) \
and abs(left["wavelength"].value - right["wavelength"].value)/left["wavelength"].value > .05:
raise RuntimeError("Cannot add incompatible wavelengths (%f != %f)"
% (left["wavelength"].value, right["wavelength"].value))
#pylint: disable=too-many-arguments
def _focusAndSum(self, filenames, preserveEvents=True, final_name=None, absorptionWksp=''):
"""Load, sum, and focus data in chunks
Purpose:
Load, sum and focus data in chunks;
Requirements:
1. input run numbers are in numpy array or list
Guarantees:
The experimental runs are focused and summed together
@param run_number_list:
@param extension:
@param preserveEvents:
@param absorptionWksp: will be divided from the data at a per-pixel level
@return: string as the summed workspace's name
"""
if final_name is None:
final_name = getBasename(filenames[0])
# only pass in the characterizations if the values haven't already been determined
characterizations = self._charTable
if self._info is not None:
characterizations = ''
# put together a list of the other arguments
otherArgs = self._focusPos.copy()
# use the workspaces if they already exists, or pass the filenames down
# this assumes that AlignAndFocusPowderFromFiles will give the workspaces the canonical names
cal, grp, msk = [self._instrument + name for name in ['_cal', '_group', '_mask']]
if mtd.doesExist(cal) and mtd.doesExist(grp) and mtd.doesExist(msk):
otherArgs['CalibrationWorkspace'] = cal
otherArgs['GroupingWorkspace'] = grp
otherArgs['MaskWorkspace'] = msk
else:
otherArgs['CalFileName'] = self.calib
otherArgs['GroupFilename'] = self.getProperty("GroupingFile").value
api.AlignAndFocusPowderFromFiles(Filename=','.join(filenames),
OutputWorkspace=final_name,
AbsorptionWorkspace=absorptionWksp,
MaxChunkSize=self._chunks,
FilterBadPulses=self._filterBadPulses,
Characterizations=characterizations,
CacheDir=self._cache_dir,
Params=self._binning,
ResampleX=self._resampleX,
Dspacing=self._bin_in_dspace,
PreserveEvents=preserveEvents,
RemovePromptPulseWidth=self._removePromptPulseWidth,
CompressTolerance=self.COMPRESS_TOL_TOF,
LorentzCorrection=self._lorentz,
UnwrapRef=self._LRef,
LowResRef=self._DIFCref,
LowResSpectrumOffset=self._lowResTOFoffset,
CropWavelengthMin=self._wavelengthMin,
CropWavelengthMax=self._wavelengthMax,
FrequencyLogNames=self.getProperty("FrequencyLogNames").value,
WaveLengthLogNames=self.getProperty("WaveLengthLogNames").value,
ReductionProperties="__snspowderreduction",
**otherArgs)
#TODO make sure that this funny function is called
#self.checkInfoMatch(info, tempinfo)
# allow for not normalizing by current for this particular one
if self._normalisebycurrent:
api.NormaliseByCurrent(InputWorkspace=final_name,
OutputWorkspace=final_name,
RecalculatePCharge=True)
get_workspace(final_name).getRun()['gsas_monitor'] = 1
return final_name
#pylint: disable=too-many-arguments,too-many-locals,too-many-branches
def _focusChunks(self, filename, filter_wall=(0.,0.), # noqa
splitwksp=None, preserveEvents=True):
"""
Load, (optional) split and focus data in chunks
@param filename: integer for run number
@param filter_wall: Enabled if splitwksp is defined
@param splitwksp: SplittersWorkspace (if None then no split)
@param preserveEvents:
@return: a string as the returned workspace's name or a list of strings as the returned workspaces' names
in the case that split workspace is used.
"""
# generate the workspace name
self.log().information("_focusChunks(): run = %s" % (filename))
# get chunk strategy for parallel processing (MPI)
strategy = self._getStrategy(filename)
# determine event splitting by checking filterWall and number of output workspaces from _focusChunk
do_split_raw_wksp, num_out_wksp = self._determine_workspace_splitting(splitwksp, filter_wall)
# Set up the data structure to hold and control output workspaces
output_wksp_list = [None] * num_out_wksp
is_first_chunk_list = [True] * num_out_wksp
self.log().debug("F1141A: Number of workspace to process = %d" % num_out_wksp)
# reduce data by chunks
chunk_index = -1
base_name = getBasename(filename)
for chunk in strategy:
# progress on chunk index
chunk_index += 1
# Load chunk, i.e., partial data into Mantid
raw_ws_name_chunk = self._load_event_data(filename, filter_wall,
out_ws_name=None,
**chunk)
if self._info is None:
self._info = self._getinfo(raw_ws_name_chunk)
# Log information for current chunk
self.__logChunkInfo(chunk)
if is_event_workspace(raw_ws_name_chunk):
# Event workspace
self.log().debug("F1141C: There are %d events after data is loaded in workspace %s." % (
get_workspace(raw_ws_name_chunk).getNumberEvents(), raw_ws_name_chunk))
# Split the workspace if it is required
if do_split_raw_wksp is True:
output_ws_name_list = self._split_workspace(raw_ws_name_chunk, splitwksp.name())
else:
# Histogram data
output_ws_name_list = [raw_ws_name_chunk]
# ENDIF
# check
if num_out_wksp != len(output_ws_name_list):
self.log().warning('Projected number of output workspaces %d must be same as '
'that of real output workspaces %d.' % (num_out_wksp, len(output_ws_name_list)))
num_out_wksp = output_ws_name_list
# log
msg = "[Fx1142] Workspace of chunk %d is %d (vs. estimated %d). \n" % (
chunk_index, len(output_ws_name_list), num_out_wksp)
for iws in range(len(output_ws_name_list)):
ws = output_ws_name_list[iws]
msg += "%s\t\t" % (str(ws))
if iws %5 == 4:
msg += "\n"
self.log().debug(msg)
# Do align and focus
num_out_wksp = len(output_ws_name_list)
for split_index in range(num_out_wksp):
# Get workspace name
out_ws_name_chunk_split = output_ws_name_list[split_index]
# Align and focus
# focuspos = self._focusPos
self.log().notice('Align and focus workspace %s' % out_ws_name_chunk_split)
api.AlignAndFocusPowder(InputWorkspace=out_ws_name_chunk_split,
OutputWorkspace=out_ws_name_chunk_split,
CalFileName=self.calib,
GroupFilename=self.getProperty("GroupingFile").value,
Params=self._binning,
ResampleX=self._resampleX,
Dspacing=self._bin_in_dspace,
PreserveEvents=preserveEvents,
RemovePromptPulseWidth=self._removePromptPulseWidth,
CompressTolerance=self.COMPRESS_TOL_TOF,
LorentzCorrection=self._lorentz,
UnwrapRef=self._LRef,
LowResRef=self._DIFCref,
LowResSpectrumOffset=self._lowResTOFoffset,
CropWavelengthMin=self._wavelengthMin,
CropWavelengthMax=self._wavelengthMax,
ReductionProperties="__snspowderreduction",
**self._focusPos)
# logging (ignorable)
if is_event_workspace(out_ws_name_chunk_split):
self.log().information('After being aligned and focused, workspace %s: Number of events = %d '
'of chunk %d ' % (out_ws_name_chunk_split,
get_workspace(out_ws_name_chunk_split).getNumberEvents(),
chunk_index))
# END-FOR-Splits
# Merge among chunks
for split_index in range(num_out_wksp):
# determine the final workspace name
final_out_ws_name = base_name
if num_out_wksp > 1:
final_out_ws_name += '_%d' % split_index
if is_first_chunk_list[split_index] is True:
# Rename if it is the first chunk that is finished
self.log().debug("[F1145] Slot %d is renamed to %s" % (split_index, final_out_ws_name))
api.RenameWorkspace(InputWorkspace=output_ws_name_list[split_index],
OutputWorkspace=final_out_ws_name)
output_wksp_list[split_index] = final_out_ws_name
is_first_chunk_list[split_index] = False
else:
# Add this chunk of workspace to the final one
clear_rhs_ws = allEventWorkspaces(output_wksp_list[split_index], output_ws_name_list[split_index])
api.Plus(LHSWorkspace=output_wksp_list[split_index],
RHSWorkspace=output_ws_name_list[split_index],
OutputWorkspace=output_wksp_list[split_index],
ClearRHSWorkspace=clear_rhs_ws)