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BASISReduction.py
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BASISReduction.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 +
# Mantid Repository : https://github.com/mantidproject/mantid
# pylint: disable=no-init
import os
import re
import numpy as np
from collections import namedtuple
from contextlib import contextmanager
import json
import random
import string
import mantid.simpleapi as sapi
from mantid.api import (mtd, PythonAlgorithm, AlgorithmFactory, FileProperty,
FileAction, AnalysisDataService, ExperimentInfo)
from mantid.kernel import (IntArrayProperty, StringListValidator,
FloatArrayProperty, EnabledWhenProperty,
Direction, PropertyCriterion)
from mantid import config as mantid_config
from os.path import join as pjoin
temp_prefix = '_ti_' # marks a workspace as temporary
def unique_workspace_name(n=3, prefix='', suffix=''):
r"""
Create a random sequence of `n` lowercase characters that is guaranteed
not to collide with the name of any existing Mantid workspace registered
in the analysis data service.
Parameters
----------
n: int
Size of the sequence
prefix: str
String to prefix the randon sequence
suffix: str
String to suffix the randon sequence
Returns
-------
str
"""
n_seq = ''.join(random.choice(string.ascii_lowercase) for _ in range(n))
ws_name = '{}{}{}'.format(str(prefix), n_seq, str(suffix))
while ws_name in AnalysisDataService.getObjectNames():
characters = [random.choice(string.ascii_lowercase) for _ in range(n)]
n_seq = ''.join(characters)
ws_name = '{}{}{}'.format(str(prefix), n_seq, str(suffix))
return ws_name
def tws(marker=''):
r"""
String starting with the temp_prefix
and guaranteed not to collide
with the name of any existing Mantid workspace
in the analysis data service
Parameters
----------
marker: str
String to identify the data contained in the workspace. Used as suffix
Returns
-------
str
"""
return unique_workspace_name(prefix=temp_prefix, suffix='_'+marker)
@contextmanager
def pyexec_setup(remove_temp, new_options):
"""
Backup keys of mantid.config
and clean up temporary files and workspaces
upon algorithm completion or exception raised.
Workspaces with names beginning
with the temporary workspace marker
are assumed temporary.
Parameters
----------
remove_temp: bool
Determine wether to remove the temporary workspaces
new_options: dict
Dictionary of mantid configuration options to be modified.
Yields
------
namedtuple:
tuple containing two lists.
The first list to hold temporary workspaces of arbitrary names.
The second list to hold temporary file names.
Used to delete the workspaces and files upon algorithm completion.
"""
temp_objects = namedtuple('temp_objects', 'files workspaces')
temps = temp_objects(list(), list())
previous_config = dict()
for key, value in new_options.items():
previous_config[key] = mantid_config[key]
mantid_config[key] = value
try:
yield temps
finally:
# reinstate the mantid options
for key, value in previous_config.items():
mantid_config[key] = value
if remove_temp is False:
return
# delete temporary files
for file_name in temps.files:
os.remove(file_name)
to_be_removed = set()
for name in AnalysisDataService.getObjectNames():
if temp_prefix in name:
to_be_removed.add(name)
for workspace in temps.workspaces:
if isinstance(workspace, str) and AnalysisDataService.doesExist(workspace):
to_be_removed.add(workspace)
else:
to_be_removed.add(workspace.name())
for name in to_be_removed:
sapi.DeleteWorkspace(name)
TEMPERATURE_SENSOR = 'SensorA'
DEFAULT_MASK_GROUP_DIR = '/SNS/BSS/shared/autoreduce/new_masks_08_12_2015'
DEFAULT_CONFIG_DIR = mantid_config['instrumentDefinition.directory']
# BASIS allows two possible reflections, with associated default properties
# pylint: disable=line-too-long
REFLECTIONS_DICT = {
'silicon_111': {'name': 'silicon_111',
'energy_bins': [-150, 0.4, 500], # micro-eV
'q_bins': [0.3, 0.2, 1.9], # inverse Angst
'banks': 'bank1,bank3,bank4',
'mask_file': 'BASIS_Mask_default_111.xml',
'default_energy': 2.0826, # meV
'vanadium_bins': [-0.0034, 0.068, 0.0034],
'vanadium_wav_range': [6.20, 6.33]},
'silicon_333': {'name': 'silicon_333',
'energy_bins': [-1500, 3.2, 5000],
'q_bins': [0.9, 0.2, 5.7],
'banks': 'bank1,bank3,bank4',
'mask_file': 'BASIS_Mask_default_333.xml',
'default_energy': 18.7434,
'vanadium_bins': [-0.0333, 0.0666, 0.0333],
'vanadium_wav_range': [2.080, 2.108]},
'silicon_311': {'name': 'silicon_311',
'energy_bins': [-740, 1.6, 740],
'q_bins': [0.5, 0.2, 3.7],
'banks': 'bank2',
'mask_file': 'BASIS_Mask_default_311.xml',
'default_energy': 7.6368, # meV
'vanadium_bins': [-0.015, 0.030, 0.015],
'vanadium_wav_range': [3.263, 3.295]}
}
# pylint: disable=too-many-instance-attributes
class BASISReduction(PythonAlgorithm):
_short_inst = None
_long_inst = None
_extension = None
_doIndiv = None
_flux_normalization_types = ['Monitor', 'Proton Charge', 'Duration']
_groupDetOpt = None
_overrideMask = None
_run_list = None # a list of runs, or a list of sets of runs
_samWsRun = None
_samSqwWs = None
def __init__(self):
PythonAlgorithm.__init__(self)
self._normalizeToFirst = False
self._as_json = None
self._temps = None # hold names of temporary workspaces and files
# properties related to the sample
self._samWs = None # name of the sample events file
# properties related to flux normalization
self._flux_normalization_type = None # default to no flux normalizat.
self._MonNorm = False # flux normalization by monitor
# properties related to the chosen reflection
self._reflection = None # entry in the reflections dictionary
self._etBins = None
self._qBins = None
# properties related to the mask
self._maskFile = None
self._dMask = None # list of masked detectors
self._maskWs = None # name of the mask workspace
# properties related to division by Vanadium (normalization)
self._doNorm = None # stores the selected item from normalization_types
self._normalizationType = None
self._normRange = None
self._norm_run_list = None
self._normWs = None
self._normMonWs = None
self._normMask = None # name of vanadium mask workspace
# properties related to saving NSXPE file
self._nsxpe_do = False
self._nxspe_psi_angle_log = None
self._nxspe_offset = 0.0
def category(self):
return 'Inelastic\\Reduction'
def name(self):
return 'BASISReduction'
def version(self):
return 1
def summary(self):
return 'Multiple-file BASIS reduction for its two reflections.'
def PyInit(self):
self._short_inst = 'BSS'
self._long_inst = 'BASIS'
self._extension = '_event.nxs'
self.declareProperty('RunNumbers', '', 'Sample run numbers')
self.declareProperty('DoIndividual', False, 'Do each run individually')
self.declareProperty('ExcludeTimeSegment', '',
'Exclude a contigous time segment; '
+ 'Examples: "71546:0-60" filter run 71546 from '
+ 'start to 60 seconds, "71546:300-600", '
+ '"71546:120-end" from 120s to the end of the run')
help_doc = """Only retain events occurring within a time segment.
Examples: 71546:0-3600 only retains events from the first hour of run 71546.
71546:3600-7200 retain only the second hour. 71546:7200-end retain events after
the first two hours"""
self.declareProperty('RetainTimeSegment', '', help_doc)
grouping_type = ['None', 'Low-Resolution', 'By-Tube']
self.declareProperty('GroupDetectors', 'None',
StringListValidator(grouping_type),
'Switch for grouping detectors')
#
# Normalization selector
#
title_flux_normalization = 'Flux Normalization'
self.declareProperty('DoFluxNormalization', True,
direction=Direction.Input,
doc='Do we normalize data by incoming flux?')
self.setPropertyGroup('DoFluxNormalization', title_flux_normalization)
if_flux_normalization = EnabledWhenProperty('DoFluxNormalization',
PropertyCriterion.IsDefault)
default_flux_normalization = self._flux_normalization_types[0]
self.declareProperty('FluxNormalizationType',
default_flux_normalization,
StringListValidator(
self._flux_normalization_types),
'Flux Normalization Type')
self.setPropertySettings('FluxNormalizationType',
if_flux_normalization)
self.setPropertyGroup('FluxNormalizationType',
title_flux_normalization)
self.declareProperty('NormalizeToFirst', False, 'Normalize spectra '
+ 'to intensity of spectrum with lowest Q?')
#
# Properties affected by the reflection selected
#
titleReflection = 'Reflection Selector'
available_reflections = sorted(REFLECTIONS_DICT.keys())
default_reflection = REFLECTIONS_DICT['silicon_111']
self.declareProperty('ReflectionType', default_reflection['name'],
StringListValidator(available_reflections),
'Analyzer. Documentation lists typical \
associated property values.')
self.setPropertyGroup('ReflectionType', titleReflection)
self.declareProperty(FloatArrayProperty('EnergyBins',
default_reflection['energy_bins'],
direction=Direction.Input),
'Energy transfer binning scheme (in ueV)')
self.setPropertyGroup('EnergyBins', titleReflection)
self.declareProperty(FloatArrayProperty('MomentumTransferBins',
default_reflection['q_bins'],
direction=Direction.Input),
'Momentum transfer binning scheme')
self.setPropertyGroup('MomentumTransferBins', titleReflection)
self.declareProperty(FileProperty(name='MaskFile', defaultValue='',
action=FileAction.OptionalLoad,
extensions=['.xml']),
'See documentation for latest mask files.')
self.setPropertyGroup('MaskFile', titleReflection)
# Properties setting the division by vanadium
titleDivideByVanadium = 'Normalization by Vanadium'
self.declareProperty('DivideByVanadium', False,
direction=Direction.Input,
doc='Do we normalize by the vanadium intensity?')
self.setPropertyGroup('DivideByVanadium', titleDivideByVanadium)
ifDivideByVanadium = EnabledWhenProperty('DivideByVanadium',
PropertyCriterion.IsNotDefault)
normalization_types = ['by Q slice', 'by detector ID']
self.declareProperty('NormalizationType', 'by Q slice',
StringListValidator(normalization_types),
'Select a Vanadium normalization')
self.setPropertySettings('NormalizationType', ifDivideByVanadium)
self.setPropertyGroup('NormalizationType', titleDivideByVanadium)
self.declareProperty('NormRunNumbers', '', 'Normalization run numbers')
self.setPropertySettings('NormRunNumbers', ifDivideByVanadium)
self.setPropertyGroup('NormRunNumbers', titleDivideByVanadium)
# Properties setting the saving of NSXPE file
title_nxspe = 'Save to NXSPE'
self.declareProperty('SaveNXSPE', False, direction=Direction.Input,
doc='Do we save to NXSPE format?')
nxspe_enabled = EnabledWhenProperty('SaveNXSPE',
PropertyCriterion.IsNotDefault)
self.setPropertyGroup('SaveNXSPE', title_nxspe)
self.declareProperty('PsiAngleLog', 'SE50Rot', direction=Direction.Input,
doc='name of entry in the logs storing the psi \
angle')
self.setPropertySettings('PsiAngleLog', nxspe_enabled)
self.setPropertyGroup('PsiAngleLog', title_nxspe)
self.declareProperty('PsiOffset', 0.0, direction=Direction.Input,
doc='add this quantity to the psi angle stored \
in the log')
self.setPropertySettings('PsiOffset', nxspe_enabled)
self.setPropertyGroup('PsiOffset', title_nxspe)
# Aditional output properties
titleAddionalOutput = 'Additional Output'
self.declareProperty('OutputSusceptibility', False,
direction=Direction.Input,
doc='Output dynamic susceptibility (Xqw)')
self.setPropertyGroup('OutputSusceptibility', titleAddionalOutput)
self.declareProperty('OutputPowderSpectrum', False,
direction=Direction.Input,
doc='Output S(Q) and S(theta) powder diffraction')
self.setPropertyGroup('OutputPowderSpectrum', titleAddionalOutput)
self.declareProperty('RemoveTemporaryWorkspaces', True, direction=Direction.Input,
doc='Remove temporary workspaces and files')
self.setPropertyGroup('RemoveTemporaryWorkspaces', titleAddionalOutput)
# pylint: disable=too-many-branches
def PyExec(self):
# Facility and database configuration
config_new_options = {'default.facility': 'SNS',
'default.instrument': 'BASIS',
'datasearch.searcharchive': 'On'}
#
# implement with ContextDecorator after python2 is deprecated)
#
remove_temp = self.getProperty('RemoveTemporaryWorkspaces').value
with pyexec_setup(remove_temp, config_new_options) as self._temps:
self._PyExec()
def _PyExec(self):
# Collect Flux Normalization
if self.getProperty('DoFluxNormalization').value is True:
self._flux_normalization_type =\
self.getProperty('FluxNormalizationType').value
if self._flux_normalization_type == 'Monitor':
self._MonNorm = True
self._reflection =\
REFLECTIONS_DICT[self.getProperty('ReflectionType').value]
self._doIndiv = self.getProperty('DoIndividual').value
# micro-eV to mili-eV
self._etBins = 1.E-03 * self.getProperty('EnergyBins').value
self._qBins = self.getProperty('MomentumTransferBins').value
self._qBins[0] -= self._qBins[1]/2.0 # leftmost bin boundary
self._qBins[2] += self._qBins[1]/2.0 # rightmost bin boundary
self._maskFile = self.getProperty('MaskFile').value
maskfile = self.getProperty('MaskFile').value
self._maskFile = maskfile if maskfile else\
pjoin(DEFAULT_MASK_GROUP_DIR, self._reflection['mask_file'])
self._groupDetOpt = self.getProperty('GroupDetectors').value
self._normalizeToFirst = self.getProperty('NormalizeToFirst').value
self._doNorm = self.getProperty('DivideByVanadium').value
# retrieve properties pertaining to saving to NXSPE file
self._nsxpe_do = self.getProperty('SaveNXSPE').value
if self._nsxpe_do:
self._nxspe_psi_angle_log = self.getProperty('PsiAngleLog').value
self._nxspe_offset = self.getProperty('PsiOffset').value
# Apply default mask if not supplied by user
self._overrideMask = bool(self._maskFile)
if not self._overrideMask:
mantid_config.appendDataSearchDir(DEFAULT_MASK_GROUP_DIR)
self._maskFile = self._reflection['mask_file']
self._maskWs = tws('BASIS_MASK')
sapi.LoadMask(Instrument='BASIS',
OutputWorkspace=self._maskWs,
InputFile=self._maskFile)
# Work around length issue
_dMask = sapi.ExtractMask(InputWorkspace=self._maskWs,
OutputWorkspace=tws('ExtractMask'))
self._dMask = _dMask[1]
#
# Process the Vanadium
#
norm_runs = self.getProperty('NormRunNumbers').value
if self._doNorm and bool(norm_runs):
self._normalizationType = self.getProperty('NormalizationType').value
self.log().information('Divide by Vanadium with normalization' + self._normalizationType)
# Following steps common to all types of Vanadium normalization
# norm_runs encompasses a single set, thus _getRuns returns
# a list of only one item
norm_set = self._get_runs(norm_runs, doIndiv=False)[0]
normWs = tws(self._make_run_name(norm_set[0]) + '_vanadium')
self._sum_and_calibrate(norm_set, normWs)
normRange = self._reflection['vanadium_wav_range']
bin_width = normRange[1] - normRange[0]
# This rebin integrates counts onto a histogram of a single bin
if self._normalizationType == 'by detector ID':
self._normRange = [normRange[0], bin_width, normRange[1]]
sapi.Rebin(InputWorkspace=normWs,
OutputWorkspace=normWs,
Params=self._normRange)
self._normWs = normWs
# Detectors outside limits are substituted by MedianDetectorTest
self._normMask = tws('BASIS_NORM_MASK')
sapi.FindDetectorsOutsideLimits(InputWorkspace=normWs,
LowThreshold=1.0*bin_width,
# no count events outside ranges
RangeLower=normRange[0],
RangeUpper=normRange[1],
OutputWorkspace=self._normMask)
# additional reduction steps when normalizing by Q slice
if self._normalizationType == 'by Q slice':
self._normWs = self._group_and_SofQW(normWs, normWs,
self._etBins,
isSample=False)
#
# Process the sample
#
self._run_list = self._get_runs(self.getProperty('RunNumbers').value,
doIndiv=self._doIndiv)
for run_set in self._run_list:
self._samWs = tws(self._make_run_name(run_set[0]))
self._sum_and_calibrate(run_set, self._samWs)
self._samWsRun = str(run_set[0])
# Divide by Vanadium detector ID, if pertinent
if self._normalizationType == 'by detector ID':
# Mask detectors with low Vanadium signal before dividing
sapi.MaskDetectors(Workspace=self._samWs,
MaskedWorkspace=self._normMask)
sapi.Divide(LHSWorkspace=self._samWs,
RHSWorkspace=self._normWs,
OutputWorkspace=self._samWs)
# additional reduction steps
prefix = self._make_run_name(run_set[0])
self._samSqwWs = self._group_and_SofQW(self._samWs, prefix,
self._etBins, isSample=True)
# Divide by Vanadium Q slice, if pertinent
if self._normalizationType == 'by Q slice':
sapi.Divide(LHSWorkspace=self._samSqwWs,
RHSWorkspace=self._normWs,
OutputWorkspace=self._samSqwWs)
# Clear mask from reduced file. Needed for binary operations
# involving this S(Q,w)
sapi.ClearMaskFlag(Workspace=self._samSqwWs)
# Scale so that elastic line has Y-values ~ 1
if self._normalizeToFirst:
self._ScaleY(self._samSqwWs)
# Transform the vertical axis (Q) to point data
# Q-values are in X-axis now
sapi.Transpose(InputWorkspace=self._samSqwWs,
OutputWorkspace=self._samSqwWs)
# from histo to point
sapi.ConvertToPointData(InputWorkspace=self._samSqwWs,
OutputWorkspace=self._samSqwWs)
# Q-values back to vertical axis
sapi.Transpose(InputWorkspace=self._samSqwWs,
OutputWorkspace=self._samSqwWs)
self.serialize_in_log(self._samSqwWs) # store the call
# Output Dave and Nexus files
extension = '_divided.dat' if self._doNorm else '.dat'
dave_grp_filename = self._make_run_name(self._samWsRun, False) + \
extension
sapi.SaveDaveGrp(Filename=dave_grp_filename,
InputWorkspace=self._samSqwWs,
ToMicroEV=True)
extension = '_divided_sqw.nxs' if self._doNorm else '_sqw.nxs'
processed_filename = self._make_run_name(self._samWsRun, False) + \
extension
sapi.SaveNexus(Filename=processed_filename,
InputWorkspace=self._samSqwWs)
# additional output
if self.getProperty('OutputSusceptibility').value is True:
temperature = mtd[self._samSqwWs].getRun().\
getProperty(TEMPERATURE_SENSOR).getStatistics().mean
samXqsWs = self._samSqwWs.replace('sqw', 'Xqw')
sapi.ApplyDetailedBalance(InputWorkspace=self._samSqwWs,
OutputWorkspace=samXqsWs,
Temperature=str(temperature))
sapi.ConvertUnits(InputWorkspace=samXqsWs,
OutputWorkspace=samXqsWs,
Target='DeltaE_inFrequency',
Emode='Indirect',
Efixed=self._reflection['default_energy'])
self.serialize_in_log(samXqsWs)
susceptibility_filename = processed_filename.replace('sqw', 'Xqw')
sapi.SaveNexus(Filename=susceptibility_filename,
InputWorkspace=samXqsWs)
if self.getProperty('OutputPowderSpectrum').value:
self.generatePowderSpectrum()
def _get_runs(self, rlist, doIndiv=True):
r"""
Create sets of run numbers for analysis. A semicolon indicates a
separate group of runs to be processed together.
Parameters
----------
rlist: str
All the run numbers to be reduced.
doIndiv: bool
Return each run on its own list
Returns
-------
list
Items of this list are lists. If `doIndiv` is True, each item
list is made up of a single run number. If `doIndiv` is False,
each item list is made up of several run numbers which are to
be reduced together.
"""
run_list = []
# ';' separates the runs into substrings. Each substring
# represents a set of runs
rlvals = rlist.split(';')
for rlval in rlvals:
iap = IntArrayProperty('_get_runs_iap', rlval) # substring split
if doIndiv:
run_list.extend([[str(x)] for x in iap.value])
else:
run_list.append([str(x) for x in iap.value])
return run_list
def _make_run_name(self, run, useShort=True):
r"""
Make name like BSS_24234, for instance
Parameters
----------
run: str
Run number
useShort: bool
Whether to use 'BSS' or 'BASIS'
Returns
-------
str
Identifier of instrument and run numbers
"""
if useShort:
return self._short_inst + '_' + str(run)
else:
return self._long_inst + '_' + str(run)
def _make_run_file(self, run):
r"""
Make name like BSS_24234_event.nxs
Parameters
----------
run: str
Run number
Returns
-------
str
events file name
"""
return '{0}_{1}_event.nxs'.format(self._short_inst, str(run))
def _sum_runs(self, run_set, sam_ws):
r"""
Aggregate the set of runs
Parameters
----------
run_set: list
Run numbers
sam_ws: str
Name of aggregate workspace for the sample
extra_ext: str
Suffix to be added to the temporary workspaces
"""
self.load_single_run(run_set[0], sam_ws)
for run in run_set[1:]:
ws_name = tws('sum_runs_'+run)
self.load_single_run(run, ws_name)
sapi.Plus(LHSWorkspace=sam_ws,
RHSWorkspace=ws_name,
OutputWorkspace=sam_ws)
def load_single_run(self, run, name):
r"""
Find and load events.
Applies event filtering if necessary.
Parameters
----------
run: str
Run number
name: str
Name of the output EventsWorkspace
Returns
-------
EventsWorkspace
"""
kwargs = dict(Filename=self._make_run_file(run),
BankName=self._reflection['banks'],
OutputWorkspace=name)
if str(run) + ':' in self.getProperty('RetainTimeSegment').value:
kwargs.update(self._retainEvents(run))
sapi.LoadEventNexus(**kwargs)
if str(run) + ':' in self.getProperty('ExcludeTimeSegment').value:
self._filterEvents(run, name)
def _sum_monitors(self, run_set, mon_ws):
r"""
Generate aggregate monitor workspace from a list of run numbers
Parameters
----------
run_set: list
List of run numbers
mon_ws: str
Name of output workspace
"""
sapi.LoadNexusMonitors(Filename=self._make_run_file(run_set[0]),
OutputWorkspace=mon_ws)
for run in run_set[1:]:
ws_name = tws('sum_monitors_'+run)
sapi.LoadNexusMonitors(Filename=self._make_run_file(run),
OutputWorkspace=ws_name)
sapi.Plus(LHSWorkspace=mon_ws,
RHSWorkspace=ws_name,
OutputWorkspace=mon_ws)
def _generate_flux_spectrum(self, run_set, sam_ws):
r"""
Retrieve the aggregate flux and create an spectrum of intensities
versus wavelength such that intensities will be similar for any
of the possible flux normalization types.
Parameters
----------
sam_ws: str
Name of aggregated sample workspace
Returns
-------
str
Name of aggregated flux workspace (output workspace)
"""
flux_binning = [1.5, 0.0005, 7.5] # wavelength binning
suffix = re.sub('[^0-9a-zA-Z]+', '_', self._flux_normalization_type)
flux_ws = tws(self._make_run_name(run_set[0]) + '_' + suffix)
if self._MonNorm:
self._sum_monitors(run_set, flux_ws)
rpf = self._elucidate_reflection_parameter_file(sam_ws)
sapi.LoadParameterFile(Workspace=flux_ws, Filename=rpf)
sapi.ModeratorTzeroLinear(InputWorkspace=flux_ws,
OutputWorkspace=flux_ws)
sapi.Rebin(InputWorkspace=flux_ws,
OutputWorkspace=flux_ws,
Params='10', # 10 microseconds TOF bin width
PreserveEvents=False)
sapi.ConvertUnits(InputWorkspace=flux_ws,
OutputWorkspace=flux_ws,
Target='Wavelength')
sapi.OneMinusExponentialCor(InputWorkspace=flux_ws,
OutputWorkspace=flux_ws,
C='0.20749999999999999',
C1='0.001276')
sapi.Scale(InputWorkspace=flux_ws, OutputWorkspace=flux_ws,
Factor='1e-06')
sapi.Rebin(InputWorkspace=flux_ws, OutputWorkspace=flux_ws,
Params=flux_binning)
else:
ws = mtd[sam_ws].getRun()
if self._flux_normalization_type == 'Proton Charge':
aggregate_flux = ws.getProtonCharge()
elif self._flux_normalization_type == 'Duration':
aggregate_flux = ws.getProperty('duration').value
# These factors ensure intensities typical of flux workspaces
# derived from monitor data
f = {'Proton Charge': 0.00874, 'Duration': 0.003333}
x = np.arange(flux_binning[0], flux_binning[2], flux_binning[1])
y = f[self._flux_normalization_type] * \
aggregate_flux * np.ones(len(x) - 1)
_flux_ws = sapi.CreateWorkspace(OutputWorkspace=flux_ws, DataX=x,
DataY=y, UnitX='Wavelength')
_flux_ws.setYUnit(mtd[sam_ws].YUnit())
return flux_ws
def _calibrate_data(self, run_set, sam_ws):
sapi.MaskDetectors(Workspace=sam_ws, DetectorList=self._dMask)
rpf = self._elucidate_reflection_parameter_file(sam_ws)
sapi.LoadParameterFile(Workspace=sam_ws, Filename=rpf)
sapi.ModeratorTzeroLinear(InputWorkspace=sam_ws,
OutputWorkspace=sam_ws)
sapi.ConvertUnits(InputWorkspace=sam_ws,
OutputWorkspace=sam_ws,
Target='Wavelength',
EMode='Indirect',
EFixed=self._reflection['default_energy'])
if self._flux_normalization_type is not None:
flux_ws = self._generate_flux_spectrum(run_set, sam_ws)
sapi.RebinToWorkspace(WorkspaceToRebin=sam_ws,
WorkspaceToMatch=flux_ws,
OutputWorkspace=sam_ws)
sapi.Divide(LHSWorkspace=sam_ws, RHSWorkspace=flux_ws,
OutputWorkspace=sam_ws)
def _sum_and_calibrate(self, run_set, wsName):
"""
Aggregate the set of runs and calibrate
Parameters
----------
run_set: list
Run numbers
extra_extension: str
Suffix to be added to the workspace names
Returns
-------
str
workspace name of the aggregated and calibrated data
"""
self._sum_runs(run_set, wsName)
self._calibrate_data(run_set, wsName)
def _group_and_SofQW(self, wsName, prefix, etRebins, isSample=True):
r"""
Transforms from wavelength and detector ID to S(Q,E)
Parameters
----------
wsName: str
Name of a workspace as a function of wavelength and detector id
prefix: str
Name prefix for output workspaces and files
etRebins: list
Final energy domain and bin width
isSample: bool
Discriminates between sample and vanadium
Returns
-------
str
Name of S(Q,E) workspace
"""
sapi.ConvertUnits(InputWorkspace=wsName,
OutputWorkspace=wsName,
Target='DeltaE',
EMode='Indirect',
EFixed=self._reflection['default_energy'])
sapi.CorrectKiKf(InputWorkspace=wsName,
OutputWorkspace=wsName,
EMode='Indirect',
EFixed=self._reflection['default_energy'])
sapi.Rebin(InputWorkspace=wsName,
OutputWorkspace=wsName,
Params=etRebins)
if self._groupDetOpt != 'None':
if self._groupDetOpt == 'Low-Resolution':
grp_file = 'BASIS_Grouping_LR.xml'
else:
grp_file = 'BASIS_Grouping.xml'
# If mask override used, we need to add default grouping file
# location to search paths
if self._overrideMask:
mantid_config.appendDataSearchDir(DEFAULT_MASK_GROUP_DIR)
sapi.GroupDetectors(InputWorkspace=wsName,
OutputWorkspace=wsName,
MapFile=grp_file,
Behaviour='Sum')
# Output NXSPE file (must be done before transforming the
# vertical axis to point data)
if isSample and self._nsxpe_do:
extension = '.nxspe'
run = mtd[wsName].getRun()
if run.hasProperty(self._nxspe_psi_angle_log):
psi_angle_logproperty = \
run.getProperty(self._nxspe_psi_angle_log)
psi_angle = np.average(psi_angle_logproperty.value)
psi_angle += self._nxspe_offset
nxspe_filename = prefix + extension
sapi.SaveNXSPE(InputWorkspace=wsName,
Filename=nxspe_filename,
Efixed=self._reflection['default_energy'],
Psi=psi_angle,
KiOverKfScaling=1)
else:
error_message = 'Runs have no log entry named {}'\
.format(self._nxspe_psi_angle_log)
self.log().error(error_message)
wsSqwName = prefix if isSample is True else wsName
wsSqwName += '_divided_sqw' if self._doNorm is True else '_sqw'
sapi.SofQW3(InputWorkspace=wsName,
QAxisBinning=self._qBins,
EMode='Indirect',
EFixed=self._reflection['default_energy'],
OutputWorkspace=wsSqwName)
# Rebin the vanadium within the elastic line
if not isSample:
sapi.Rebin(InputWorkspace=wsSqwName,
OutputWorkspace=wsSqwName,
Params=self._reflection['vanadium_bins'])
return wsSqwName
def _ScaleY(self, wsName):
r"""
Scale all spectra by a number so that the maximum of the first spectra
is rescaled to 1
Parameters
----------
wsName: str
Name of the workspace to rescale
"""
workspace = sapi.mtd[wsName]
maximumYvalue = workspace.dataY(0).max()
sapi.Scale(InputWorkspace=wsName,
OutputWorkspace=wsName,
Factor=1./maximumYvalue,
Operation='Multiply')
def generateSplitterWorkspace(self, fragment):
r"""
Create a table workspace with time intervals to keep
Parameters
----------
fragment: str
a-b start and end of time fragment to filter out
"""
inf = 172800 # a run two full days long
a, b = fragment.split('-')
b = inf if 'end' in b else float(b)
a = float(a)
splitter = sapi.CreateEmptyTableWorkspace(OutputWorkspace=tws('splitter'))
splitter.addColumn('double', 'start')
splitter.addColumn('double', 'stop')
splitter.addColumn('str', 'target')
if a == 0.0:
splitter.addRow([b, inf, '0'])
elif b == inf:
splitter.addRow([0, a, '0'])
else:
splitter.addRow([0, a, '0'])
splitter.addRow([b, inf, '0'])
self._temps.extend('splitted_unfiltered', 'TOFCorrectWS')
def _filterEvents(self, run, ws_name):
r"""
Filter out ExcludeTimeSegment if applicable
Parameters
----------
run: str
run number
ws_name : str
name of the workspace to filter
"""
for run_fragment in self.getProperty('ExcludeTimeSegment').value.split(','):
if run+':' in run_fragment:
self.generateSplitterWorkspace(run_fragment.split(':')[1])
sapi.FilterEvents(InputWorkspace=ws_name,
SplitterWorkspace='splitter',
OutputWorkspaceBaseName='splitted',
GroupWorkspaces=True,
OutputWorkspaceIndexedFrom1=True,
RelativeTime=True)
sapi.UnGroupWorkspace('splitted')
sapi.RenameWorkspace(InputWorkspace='splitted_0',
OutputWorkspace=ws_name)
break
def _retainEvents(self, run):
r"""
Retain only events in a time segment
Parameters
----------
run: str
run number
ws_name : str
name of the workspace to filter
"""
inf = 172800 # a run two full days long
for run_fragment in self.getProperty('RetainTimeSegment').value.split(','):
if str(run) + ':' in run_fragment:
a, b = run_fragment.split(':')[1].split('-')
b = inf if 'end' in b else float(b)
return dict(FilterByTimeStart=float(a),
FilterByTimeStop=float(b))
raise RuntimeError('Run {} not in RetainTimeSegment '.format(run))
def serialize_in_log(self, ws_name):
r"""Save the serialization of the algorithm in the logs.
Parameters
----------
ws_name: str
Name of the workspace from which to retrieve and modify the logs
"""
def jsonify(value):
r"""Cast non-standard objects to their closest standard
representation to enable JSON serialiation"""
if isinstance(value, np.ndarray):
return value.tolist()
return value
if self._as_json is None:
self._as_json = json.loads(str(self))
# Force serialization of the following properties even if having
# their default values
forced = {name: jsonify(self.getProperty(name).value)
for name in ('DoIndividual', 'DoFluxNormalization',
'FluxNormalizationType', 'NormalizeToFirst',
'ReflectionType', 'EnergyBins',
'MomentumTransferBins', 'MaskFile',
'DivideByVanadium')}
self._as_json['properties'].update(forced)
r = mtd[ws_name].mutableRun()
r.addProperty('asString', json.dumps(self._as_json), True)
def _elucidate_reflection_parameter_file(self, ws_name):
r"""
Search a parameter file for the selected reflection and with
valid-from, valid-to dates framing the starting date for the run(s)
Parameters
----------
ws_name: str
Name of the workspace from which to retrieve the starting date
Returns
-------
str
Full path to the parameter file
"""
prefix = 'BASIS_{}_Parameters'.format(self._reflection['name'])
instr_directories = [DEFAULT_CONFIG_DIR]
start_date = str(mtd[ws_name].getRun().startTime()).strip()
parm_files = ExperimentInfo.getResourceFilenames(prefix,
['xml'],
instr_directories,
start_date)
parm_file = parm_files[0] # first in the list is the most appropriate
# store selected parameter file in the logs
mtd[ws_name].getRun().addProperty('reflParmFile',
os.path.basename(parm_file), True)
return parm_file
def generatePowderSpectrum(self):
r"""Call BASISPowderReduction to generate a powder spectrum"""
powder_reducer = self.createChildAlgorithm("BASISPowderDiffraction",
enableLogging=False)
for prop in ('RunNumbers', 'DoFluxNormalization',
'FluxNormalizationType'):
powder_reducer.setProperty(prop, self.getProperty(prop).value)
# This may go later into a input property
powder_reducer.setProperty('MomentumTransferBins', [0.1, 0.02, 4.0])