def _normalize_one_spectrum(single_spectrum_ws, spline, instrument):
rebinned_spline = mantid.RebinToWorkspace(
WorkspaceToRebin=spline,
WorkspaceToMatch=single_spectrum_ws,
StoreInADS=False)
divided = mantid.Divide(LHSWorkspace=single_spectrum_ws,
RHSWorkspace=rebinned_spline,
StoreInADS=False)
if instrument.get_instrument_prefix() == "GEM":
values_replaced = mantid.ReplaceSpecialValues(InputWorkspace=divided,
NaNValue=0,
StoreInADS=False)
# crop based off max between 1000 and 2000 tof as the vanadium peak on Gem will always occur here
complete = _crop_spline_to_percent_of_max(rebinned_spline,
values_replaced,
single_spectrum_ws, 1000,
2000)
else:
complete = mantid.ReplaceSpecialValues(
InputWorkspace=divided,
NaNValue=0,
OutputWorkspace=single_spectrum_ws)
if instrument.perform_abs_vanadium_norm():
vanadium_material = spline.sample().getMaterial()
v_number_density = vanadium_material.numberDensityEffective
v_cross_section = vanadium_material.totalScatterXSection()
vanadium_shape = spline.sample().getShape()
# number density in Angstroms-3, volume in m3. Don't bother with 1E30 factor because will cancel
num_v_atoms = vanadium_shape.volume() * v_number_density
sample_material = single_spectrum_ws.sample().getMaterial()
sample_number_density = sample_material.numberDensityEffective
sample_shape = spline.sample().getShape()
num_sample_atoms = sample_shape.volume() * sample_number_density
abs_norm_factor = v_cross_section * num_v_atoms / \
(num_sample_atoms * 4 * math.pi)
logger.notice(
"Performing absolute normalisation, multiplying by factor=" +
str(abs_norm_factor))
# avoid "Variable invalidated, data has been deleted" error when debugging
output_ws_name = single_spectrum_ws.name()
abs_norm_factor_ws = mantid.CreateSingleValuedWorkspace(
DataValue=abs_norm_factor, OutputWorkspace="__abs_norm_factor_ws")
complete = mantid.Multiply(LHSWorkspace=complete,
RHSWorkspace=abs_norm_factor_ws,
OutputWorkspace=output_ws_name)
return complete
def _divide_one_spectrum_by_spline(spectrum, spline, instrument):
rebinned_spline = mantid.RebinToWorkspace(WorkspaceToRebin=spline, WorkspaceToMatch=spectrum, StoreInADS=False)
if instrument.get_instrument_prefix() == "GEM":
divided = mantid.Divide(LHSWorkspace=spectrum, RHSWorkspace=rebinned_spline, OutputWorkspace=spectrum,
StoreInADS=False)
complete = mantid.ReplaceSpecialValues(InputWorkspace=divided, NaNValue=0, StoreInADS=False)
# crop based off max between 1000 and 2000 tof as the vanadium peak on Gem will always occur here
return _crop_spline_to_percent_of_max(rebinned_spline, complete, spectrum, 1000, 2000)
divided = mantid.Divide(LHSWorkspace=spectrum, RHSWorkspace=rebinned_spline,
StoreInADS=False)
complete = mantid.ReplaceSpecialValues(InputWorkspace=divided, NaNValue=0, OutputWorkspace=spectrum)
return complete
def _run_focus(input_workspace,
tof_output_name,
vanadium_integration_ws,
vanadium_curves_ws,
df_kwarg,
full_calib,
region_calib):
simple.NormaliseByCurrent(InputWorkspace=input_workspace, OutputWorkspace=input_workspace)
input_workspace /= vanadium_integration_ws
simple.ReplaceSpecialValues(InputWorkspace=input_workspace, OutputWorkspace=input_workspace, NaNValue=0,
InfinityValue=0)
simple.ApplyDiffCal(InstrumentWorkspace=input_workspace, CalibrationWorkspace=full_calib)
ws_d = simple.ConvertUnits(InputWorkspace=input_workspace, Target='dSpacing')
focused_sample = simple.DiffractionFocussing(InputWorkspace=ws_d, **df_kwarg)
curves_rebinned = simple.RebinToWorkspace(WorkspaceToRebin=vanadium_curves_ws, WorkspaceToMatch=focused_sample)
normalised = simple.Divide(LHSWorkspace=focused_sample, RHSWorkspace=curves_rebinned,
AllowDifferentNumberSpectra=True)
simple.ApplyDiffCal(InstrumentWorkspace=normalised, CalibrationWorkspace=region_calib)
dspacing_output_name = tof_output_name + "_dSpacing"
simple.CloneWorkspace(InputWorkspace=normalised, OutputWorkspace=dspacing_output_name)
simple.ConvertUnits(InputWorkspace=normalised, OutputWorkspace=tof_output_name, Target='TOF')
simple.DeleteWorkspace(curves_rebinned)
simple.DeleteWorkspace(focused_sample)
simple.DeleteWorkspace(normalised)
simple.DeleteWorkspace(ws_d)
def apply_vanadium_corrections(self, cyclevana, i, focused_ws):
simple.LoadNexusProcessed(Filename=self.get_vanadium(i, cyclevana),
OutputWorkspace="vana")
simple.RebinToWorkspace(WorkspaceToRebin="vana",
WorkspaceToMatch=focused_ws,
OutputWorkspace="vana")
simple.Divide(LHSWorkspace=focused_ws,
RHSWorkspace="vana",
OutputWorkspace=focused_ws)
simple.DeleteWorkspace("vana")
simple.ConvertUnits(InputWorkspace=focused_ws,
OutputWorkspace=focused_ws,
Target="TOF",
EMode="Elastic")
simple.ReplaceSpecialValues(InputWorkspace=focused_ws,
OutputWorkspace=focused_ws,
NaNValue=0.0,
NaNError=0.0,
InfinityValue=0.0,
InfinityError=0.0)
def _apply_vanadium_norm(sample_ws_foc, van_ws_foc):
# divide by curves - automatically corrects for solid angle, det efficiency and lambda dep. flux
sample_ws_foc = mantid.CropWorkspace(InputWorkspace=sample_ws_foc,
OutputWorkspace=sample_ws_foc.name(),
XMin=0.45)
van_ws_foc_rb = mantid.RebinToWorkspace(
WorkspaceToRebin=van_ws_foc,
WorkspaceToMatch=sample_ws_foc,
OutputWorkspace=VAN_CURVE_REBINNED_NAME) # copy so as not to lose data
sample_ws_foc = mantid.Divide(LHSWorkspace=sample_ws_foc,
RHSWorkspace=van_ws_foc_rb,
OutputWorkspace=sample_ws_foc.name(),
AllowDifferentNumberSpectra=False)
sample_ws_foc = mantid.ReplaceSpecialValues(
InputWorkspace=sample_ws_foc,
OutputWorkspace=sample_ws_foc.name(),
NaNValue=0,
NaNError=0.0,
InfinityValue=0,
InfinityError=0.0)
return sample_ws_foc
def Norm_data(rnum, cycle):
# Load and normalize SX data
mantid.LoadRaw(
Filename='/archive/Instruments$/NDXWISH/Instrument/data/cycle_' +
cycle + '/WISH000' + str(rnum) + '.raw',
OutputWorkspace='WISH000' + str(rnum),
LoadMonitors='Separate')
# ConvertToEventWorkspace(InputWorkspace='WISH000'+str(rnum), OutputWorkspace='WISH000'+str(rnum))
mantid.CropWorkspace(InputWorkspace='WISH000' + str(rnum),
OutputWorkspace='WISH000' + str(i),
XMin=6000,
XMax=99000)
mantid.ConvertUnits(InputWorkspace='WISH000' + str(rnum),
OutputWorkspace='WISH000' + str(rnum),
Target='Wavelength')
mantid.NormaliseByCurrent(InputWorkspace='WISH000' + str(rnum),
OutputWorkspace='WISH000' + str(rnum))
# normalize By vanadium PredictPeaks
mantid.CropWorkspace(InputWorkspace='WISH000' + str(rnum),
OutputWorkspace='WISH000' + str(rnum),
XMin=0.75,
XMax=9.3)
mantid.RebinToWorkspace(WorkspaceToRebin='Vana_smoot1',
WorkspaceToMatch='WISH000' + str(rnum),
OutputWorkspace='Vana_smoot1')
mantid.Divide(LHSWorkspace='WISH000' + str(rnum),
RHSWorkspace='Vana_smoot1',
OutputWorkspace='WISH000' + str(rnum))
# remove spike in the data above 1e15 and -1e15
mantid.ReplaceSpecialValues(InputWorkspace='WISH000' + str(rnum),
OutputWorkspace='WISH000' + str(rnum),
NaNValue=0,
InfinityValue=0,
BigNumberThreshold=1e15,
SmallNumberThreshold=-1e15)
# Convert to Diffraction MD and Lorentz Correction
mantid.ConvertToDiffractionMDWorkspace(
InputWorkspace='WISH000' + str(rnum),
OutputWorkspace='WISH000' + str(rnum) + '_MD',
LorentzCorrection=True)
def read(self, number, panel, extension):
if type(number) is int:
filename = self.datafile
logger.notice("will be reading filename...{}".format(filename))
spectra_min, spectra_max = self.return_panel_van.get(panel) if self.is_vanadium else \
self.return_panel.get(panel)
if panel != 0:
output = "w{0}-{1}".format(number, panel)
else:
output = "w{}".format(number)
shared_load_files(extension, filename, output, spectra_max,
spectra_min, False)
if extension == "nxs_event":
simple.LoadEventNexus(Filename=filename,
OutputWorkspace=output,
LoadMonitors='1')
self.read_event_nexus(number, output, panel)
if extension[:10] == "nxs_event_":
label, tmin, tmax = split_string_event(extension)
output = output + "_" + label
if tmax == "end":
simple.LoadEventNexus(Filename=filename,
OutputWorkspace=output,
FilterByTimeStart=tmin,
LoadMonitors='1',
MonitorsAsEvents='1',
FilterMonByTimeStart=tmin)
else:
simple.LoadEventNexus(Filename=filename,
OutputWorkspace=output,
FilterByTimeStart=tmin,
FilterByTimeStop=tmax,
LoadMonitors='1',
MonitorsAsEvents='1',
FilterMonByTimeStart=tmin,
FilterMonByTimeStop=tmax)
self.read_event_nexus(number, output, panel)
else:
num_1, num_2 = split_run_string(number)
output = "w{0}_{1}-{2}".format(num_1, num_2, panel)
output1 = self.load_multi_run_part(extension, num_1, panel)
output2 = self.load_multi_run_part(extension, num_2, panel)
simple.MergeRuns(output1 + "," + output2, output)
simple.DeleteWorkspace(output1)
simple.DeleteWorkspace(output2)
simple.ConvertUnits(InputWorkspace=output,
OutputWorkspace=output,
Target="Wavelength",
Emode="Elastic")
lmin, lmax = Wish.LAMBDA_RANGE
simple.CropWorkspace(InputWorkspace=output,
OutputWorkspace=output,
XMin=lmin,
XMax=lmax)
monitor_run = "monitor{}".format(number)
if monitor_run not in simple.mtd:
monitor = self.process_incidentmon(number,
extension,
spline_terms=70)
else:
monitor = simple.mtd[monitor_run]
simple.NormaliseToMonitor(InputWorkspace=output,
OutputWorkspace=output + "norm1",
MonitorWorkspace=monitor)
simple.NormaliseToMonitor(InputWorkspace=output + "norm1",
OutputWorkspace=output + "norm2",
MonitorWorkspace=monitor,
IntegrationRangeMin=0.7,
IntegrationRangeMax=10.35)
simple.DeleteWorkspace(output)
simple.DeleteWorkspace(output + "norm1")
simple.RenameWorkspace(InputWorkspace=output + "norm2",
OutputWorkspace=output)
simple.ConvertUnits(InputWorkspace=output,
OutputWorkspace=output,
Target="TOF",
EMode="Elastic")
simple.ReplaceSpecialValues(InputWorkspace=output,
OutputWorkspace=output,
NaNValue=0.0,
NaNError=0.0,
InfinityValue=0.0,
InfinityError=0.0)
return output
def run_calibration(sample_ws,
vanadium_workspace,
van_integration,
bank,
spectrum_numbers,
full_inst_calib):
"""
Creates Engineering calibration files with PDCalibration
:param sample_ws: The workspace with the sample data.
:param vanadium_workspace: The workspace with the vanadium data
:param van_integration: The integration values from the vanadium corrections
:param bank: The bank to crop to, both if none.
:param spectrum_numbers: The spectrum numbers to crop to, no crop if none.
:param full_inst_calib : workspace containing the full instrument calibration
:return: The calibration output files, the vanadium curves workspace(s), and a clone of the sample file
"""
def run_pd_calibration(kwargs_to_pass):
return simple.PDCalibration(**kwargs_to_pass)
def focus_and_make_van_curves(ceria_d, vanadium_d, grouping_kwarg):
# focus ceria
focused_ceria = simple.DiffractionFocussing(InputWorkspace=ceria_d, **grouping_kwarg)
simple.ApplyDiffCal(InstrumentWorkspace=focused_ceria, ClearCalibration=True)
tof_focused = simple.ConvertUnits(InputWorkspace=focused_ceria, Target='TOF')
# focus van data
focused_van = simple.DiffractionFocussing(InputWorkspace=vanadium_d, **grouping_kwarg)
background_van = simple.EnggEstimateFocussedBackground(InputWorkspace=focused_van, NIterations='15',
XWindow=0.03)
simple.DeleteWorkspace(focused_ceria)
simple.DeleteWorkspace(focused_van)
return tof_focused, background_van
def ws_initial_process(ws):
"""Run some processing common to both the sample and vanadium workspaces"""
simple.NormaliseByCurrent(InputWorkspace=ws, OutputWorkspace=ws)
simple.ApplyDiffCal(InstrumentWorkspace=ws, CalibrationWorkspace=full_inst_calib)
simple.ConvertUnits(InputWorkspace=ws, OutputWorkspace=ws, Target='dSpacing')
return ws
def calibrate_region_of_interest(roi, df_kwarg):
focused_roi, curves_roi = focus_and_make_van_curves(ws_d, ws_van_d, df_kwarg)
simple.RenameWorkspace(curves_roi, ("curves_" + roi))
curves_output.append(curves_roi)
# final calibration of focused data
kwargs["InputWorkspace"] = focused_roi
kwargs["OutputCalibrationTable"] = "engg_calibration_" + roi
kwargs["DiagnosticWorkspaces"] = "diag_" + roi
cal_roi = run_pd_calibration(kwargs)[0]
cal_output[roi] = cal_roi
# need to clone the data as PDCalibration rebins
sample_raw = simple.CloneWorkspace(InputWorkspace=sample_ws)
ws_van = simple.CloneWorkspace(vanadium_workspace)
ws_van_d = ws_initial_process(ws_van)
# sensitivity correction
ws_van_d /= van_integration
simple.ReplaceSpecialValues(InputWorkspace=ws_van_d, OutputWorkspace=ws_van_d, NaNValue=0, InfinityValue=0)
ws_d = ws_initial_process(sample_ws)
simple.DeleteWorkspace(van_integration)
kwargs = {
"PeakPositions": Utils.default_ceria_expected_peaks(final=True),
"TofBinning": [15500, -0.0003, 52000], # using a finer binning now have better stats
"PeakWindow": 0.04,
"MinimumPeakHeight": 0.5,
"PeakFunction": 'BackToBackExponential',
"CalibrationParameters": 'DIFC+TZERO+DIFA',
"UseChiSq": True
}
cal_output = dict()
curves_output = list()
if spectrum_numbers is None:
if bank == '1' or bank is None:
df_kwarg = {"GroupingFileName": NORTH_BANK_CAL}
calibrate_region_of_interest("bank_1", df_kwarg)
if bank == '2' or bank is None:
df_kwarg = {"GroupingFileName": SOUTH_BANK_CAL}
calibrate_region_of_interest("bank_2", df_kwarg)
else:
grp_ws = Utils.create_grouping_workspace_from_spectra_list(spectrum_numbers, sample_raw)
df_kwarg = {"GroupingWorkspace": grp_ws}
calibrate_region_of_interest("Cropped", df_kwarg)
simple.DeleteWorkspace(ws_van)
simple.DeleteWorkspace("tof_focused")
cal_params = list()
# in the output calfile, rows are present for all detids, only read one from the region of interest
bank_1_read_row = 0
bank_2_read_row = 1200
for bank_cal in cal_output:
if bank_cal == "bank_1":
read = bank_1_read_row
elif bank_cal == "bank_2":
read = bank_2_read_row
else:
read = int(Utils.create_spectrum_list_from_string(spectrum_numbers)[0]) # this can be int64
row = cal_output[bank_cal].row(read)
current_fit_params = {'difc': row['difc'], 'difa': row['difa'], 'tzero': row['tzero']}
cal_params.append(current_fit_params)
return cal_params, sample_raw, curves_output
