def handle_saving_event_workspace_when_saving_as_histogram(binning, runs, def_type, inst):
ws_in_monitor = mtd[ADD_FILES_SUM_TEMPORARY_MONITORS]
if binning == 'Monitors':
mon_x = ws_in_monitor.dataX(0)
binning = str(mon_x[0])
bin_gap = mon_x[1] - mon_x[0]
binning = binning + "," + str(bin_gap)
for j in range(2, len(mon_x)):
next_bin_gap = mon_x[j] - mon_x[j-1]
if next_bin_gap != bin_gap:
bin_gap = next_bin_gap
binning = binning + "," + str(mon_x[j-1]) + "," + str(bin_gap)
binning = binning + "," + str(mon_x[len(mon_x)-1])
sanslog.notice(binning)
Rebin(InputWorkspace=ADD_FILES_SUM_TEMPORARY, OutputWorkspace='AddFilesSumTemporary_Rebin', Params=binning,
PreserveEvents=False)
# loading the nexus file using LoadNexus is necessary because it has some metadata
# that is not in LoadEventNexus. This must be fixed.
filename, ext = _make_filename(runs[0], def_type, inst)
workspace_type = get_workspace_type(filename)
if workspace_type is WorkspaceType.MultiperiodEvent:
# If we are dealing with multi-period event workspaces then there is no way of getting any other
# sample log information hence we use make a copy of the monitor workspace and use that instead
# of the reloading the first file again
CloneWorkspace(InputWorkspace=ADD_FILES_SUM_TEMPORARY_MONITORS, OutputWorkspace=ADD_FILES_SUM_TEMPORARY)
else:
LoadNexus(Filename=filename, OutputWorkspace=ADD_FILES_SUM_TEMPORARY,
SpectrumMax=ws_in_monitor.getNumberHistograms())
# User may have selected a binning which is different from the default
Rebin(InputWorkspace=ADD_FILES_SUM_TEMPORARY, OutputWorkspace=ADD_FILES_SUM_TEMPORARY, Params=binning)
# For now the monitor binning must be the same as the detector binning
# since otherwise both cannot exist in the same output histogram file
Rebin(InputWorkspace=ADD_FILES_SUM_TEMPORARY_MONITORS, OutputWorkspace=ADD_FILES_SUM_TEMPORARY_MONITORS,
Params=binning)
ws_in_monitor = mtd[ADD_FILES_SUM_TEMPORARY_MONITORS]
wsOut = mtd[ADD_FILES_SUM_TEMPORARY]
ws_in_detector = mtd['AddFilesSumTemporary_Rebin']
# We loose added sample log information since we reload a single run workspace
# and conjoin with the added workspace. In order to preserve some added sample
# logs we need to transfer them at this point
transfer_special_sample_logs(from_ws=ws_in_detector, to_ws=wsOut)
mon_n = ws_in_monitor.getNumberHistograms()
for i in range(mon_n):
wsOut.setY(i, ws_in_monitor.dataY(i))
wsOut.setE(i, ws_in_monitor.dataE(i))
ConjoinWorkspaces(wsOut, ws_in_detector, CheckOverlapping=True)
if 'AddFilesSumTemporary_Rebin' in mtd:
DeleteWorkspace('AddFilesSumTemporary_Rebin')
def setUpClass(cls):
cls.g1da = config['graph1d.autodistribution']
config['graph1d.autodistribution'] = 'On'
cls.ws2d_non_distribution = CreateWorkspace(
DataX=[10, 20, 30, 10, 20, 30],
DataY=[2, 3, 4, 5],
DataE=[1, 2, 3, 4],
NSpec=2,
Distribution=False,
UnitX='Wavelength',
YUnitLabel='Counts per microAmp.hour',
VerticalAxisUnit='DeltaE',
VerticalAxisValues=[4, 6, 8],
OutputWorkspace='ws2d_non_distribution')
cls.ws2d_distribution = CreateWorkspace(
DataX=[10, 20, 30, 10, 20, 30],
DataY=[2, 3, 4, 5, 6],
DataE=[1, 2, 3, 4, 6],
NSpec=1,
Distribution=True,
UnitX='Wavelength',
YUnitLabel='Counts per microAmp.hour',
OutputWorkspace='ws2d_distribution')
cls.ws2d_histo = CreateWorkspace(DataX=[10, 20, 30, 10, 20, 30],
DataY=[2, 3, 4, 5],
DataE=[1, 2, 3, 4],
NSpec=2,
Distribution=True,
UnitX='Wavelength',
VerticalAxisUnit='DeltaE',
VerticalAxisValues=[4, 6, 8],
OutputWorkspace='ws2d_histo')
cls.ws2d_point = CreateWorkspace(
DataX=[1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4],
DataY=[2] * 12,
NSpec=3,
OutputWorkspace='ws2d_point')
cls.ws1d_point = CreateWorkspace(DataX=[1, 2],
DataY=[1, 2],
NSpec=1,
Distribution=False,
OutputWorkspace='ws1d_point')
cls.ws2d_histo_rag = CreateWorkspace(
DataX=[1, 2, 3, 4, 5, 2, 4, 6, 8, 10],
DataY=[2] * 8,
NSpec=2,
VerticalAxisUnit='DeltaE',
VerticalAxisValues=[5, 7, 9],
OutputWorkspace='ws2d_histo_rag')
cls.ws2d_point_rag = CreateWorkspace(DataX=[1, 2, 3, 4, 2, 4, 6, 8],
DataY=[2] * 8,
NSpec=2,
OutputWorkspace='ws2d_point_rag')
cls.ws_MD_2d = CreateMDHistoWorkspace(
Dimensionality=3,
Extents='-3,3,-10,10,-1,1',
SignalInput=range(25),
ErrorInput=range(25),
NumberOfEvents=10 * np.ones(25),
NumberOfBins='5,5,1',
Names='Dim1,Dim2,Dim3',
Units='MomentumTransfer,EnergyTransfer,Angstrom',
OutputWorkspace='ws_MD_2d')
cls.ws_MD_1d = CreateMDHistoWorkspace(
Dimensionality=3,
Extents='-3,3,-10,10,-1,1',
SignalInput=range(5),
ErrorInput=range(5),
NumberOfEvents=10 * np.ones(5),
NumberOfBins='1,5,1',
Names='Dim1,Dim2,Dim3',
Units='MomentumTransfer,EnergyTransfer,Angstrom',
OutputWorkspace='ws_MD_1d')
cls.ws2d_point_uneven = CreateWorkspace(
DataX=[10, 20, 30],
DataY=[1, 2, 3],
NSpec=1,
OutputWorkspace='ws2d_point_uneven')
cls.ws2d_high_counting_detector = CreateWorkspace(
DataX=[1, 2, 3, 4] * 1000,
DataY=[2] * 4 * 12 + [200] * 4 + [2] * 987 * 4,
NSpec=1000,
OutputWorkspace='ws2d_high_counting_detector')
wp = CreateWorkspace(DataX=[15, 25, 35, 45],
DataY=[1, 2, 3, 4],
NSpec=1)
ConjoinWorkspaces(cls.ws2d_point_uneven, wp, CheckOverlapping=False)
cls.ws2d_point_uneven = mantid.mtd['ws2d_point_uneven']
cls.ws2d_histo_uneven = CreateWorkspace(
DataX=[10, 20, 30, 40],
DataY=[1, 2, 3],
NSpec=1,
OutputWorkspace='ws2d_histo_uneven')
wp = CreateWorkspace(DataX=[15, 25, 35, 45, 55],
DataY=[1, 2, 3, 4],
NSpec=1)
ConjoinWorkspaces(cls.ws2d_histo_uneven, wp, CheckOverlapping=False)
cls.ws2d_histo_uneven = mantid.mtd['ws2d_histo_uneven']
newYAxis = mantid.api.NumericAxis.create(3)
newYAxis.setValue(0, 10)
newYAxis.setValue(1, 15)
newYAxis.setValue(2, 25)
cls.ws2d_histo_uneven.replaceAxis(1, newYAxis)
AddTimeSeriesLog(cls.ws2d_histo,
Name="my_log",
Time="2010-01-01T00:00:00",
Value=100)
AddTimeSeriesLog(cls.ws2d_histo,
Name="my_log",
Time="2010-01-01T00:30:00",
Value=15)
AddTimeSeriesLog(cls.ws2d_histo,
Name="my_log",
Time="2010-01-01T00:50:00",
Value=100.2)
def PyExec(self):
data = self._expand_groups()
bkg = self.getProperty(
"BackgroundWorkspace").valueAsStr # same background for all
cal = self.getProperty(
"CalibrationWorkspace").value # same calibration for all
numberBins = self.getProperty("NumberBins").value
outWS = self.getPropertyValue("OutputWorkspace")
summing = self.getProperty("Sum").value # [Yes or No]
# convert all of the input workspaces into spectrum of "target" units (generally angle)
data, masks = self._convert_data(data)
# determine x-range
xMin, xMax = self._locate_global_xlimit(data)
# BEGIN_FOR: prcess_spectra
for n, (_wsn, _mskn) in enumerate(zip(data, masks)):
# resample spectra
ResampleX(
InputWorkspace=_wsn,
OutputWorkspace=_wsn,
XMin=xMin,
XMax=xMax,
NumberBins=numberBins,
EnableLogging=False,
)
# calibration
if cal is not None:
_ws_cal_resampled = self._resample_calibration(
_wsn, _mskn, xMin, xMax)
Divide(
LHSWorkspace=_wsn,
RHSWorkspace=_ws_cal_resampled,
OutputWorkspace=_wsn,
EnableLogging=False,
)
else:
_ws_cal_resampled = None
Scale(
InputWorkspace=_wsn,
OutputWorkspace=_wsn,
Factor=self._get_scale(cal) / self._get_scale(_wsn),
EnableLogging=False,
)
# background
if bkg:
_ws_bkg_resampled = self._resample_background(
bkg, _wsn, _mskn, xMin, xMax, _ws_cal_resampled)
Minus(
LHSWorkspace=_wsn,
RHSWorkspace=_ws_bkg_resampled,
OutputWorkspace=_wsn,
EnableLogging=False,
)
if summing:
# conjoin
if n < 1:
RenameWorkspace(
InputWorkspace=_wsn,
OutputWorkspace="__ws_conjoined",
EnableLogging=False,
)
else:
# this adds to `InputWorkspace1`
ConjoinWorkspaces(
InputWorkspace1="__ws_conjoined",
InputWorkspace2=_wsn,
CheckOverlapping=False,
EnableLogging=False,
)
# END_FOR: prcess_spectra
# Step_3: sum all spectra
# ref: https://docs.mantidproject.org/nightly/algorithms/SumSpectra-v1.html
if summing:
if cal is not None:
outWS = SumSpectra(
InputWorkspace="__ws_conjoined",
OutputWorkspace=outWS,
WeightedSum=True,
MultiplyBySpectra=not bool(cal),
EnableLogging=False,
)
else:
outWS = SumSpectra(
InputWorkspace="__ws_conjoined",
OutputWorkspace=outWS,
WeightedSum=True,
MultiplyBySpectra=True,
EnableLogging=False,
)
else:
if len(data) == 1:
outWS = RenameWorkspace(InputWorkspace=data[0],
OutputWorkspace=outWS)
else:
outWS = GroupWorkspaces(InputWorkspaces=data,
OutputWorkspace=outWS)
self.setProperty("OutputWorkspace", outWS)
# Step_4: remove temp workspaces
[
DeleteWorkspace(ws, EnableLogging=False)
for ws in self.temp_workspace_list if mtd.doesExist(ws)
]